In recent years, facial emotion recognition has gained significant improvement and attention. This technology utilizes advanced algorithms to analyze facial expressions, enabling computers to detect and interpret human emotions accurately. Its applications span over a wide range of fields, from improving customer service through sentiment analysis, to enhancing mental health support by monitoring emotional states. However, there are several challenges in facial emotion recognition, including variability in individual expressions, cultural differences in emotion display, and privacy concerns related to data collection and usage. Lighting conditions, occlusions, and the need for diverse datasets also impacts accuracy. To solve these issues, an enhanced multi-verse optimizer (EMVO) technique is proposed to improve the efficiency of recognizing emotions. Moreover, EMVO is used to improve the convergence speed, exploration-exploitation balance, solution quality, and the applicability in different types of optimization problems. Two datasets were used to collect the data, namely YouTube and surrey audio-visual expressed emotion (SAVEE) datasets. Then, the classification is done using the convolutional neural networks (CNN) to improve the performance of emotion recognition. When compared to the existing methods shuffled frog leaping algorithm-incremental wrapper-based subset selection (SFLA-IWSS), hierarchical deep neural network (H-DNN) and unique preference learning (UPL), the proposed method achieved better accuracies, measured at 98.65% and 98.76% on the YouTube and SAVEE datasets, respectively.
IRJET- Prediction of Human Facial Expression using Deep LearningIRJET Journal
This document discusses predicting human facial expressions using deep learning. It begins with an introduction to emotion recognition, facial emotion recognition, and deep learning techniques. It then reviews related literature on facial expression recognition using methods like CNNs, active appearance models, and analyzing facial features. The proposed system and methodology are described, including face registration, feature extraction focusing on action units, and using classifiers like KNN, MLP, and SVM to classify emotions based on these features. The goal is to recognize seven basic emotions (neutral, joy, sadness, surprise, anger, fear, disgust) in still images using deep learning techniques.
A Study on Face Expression Observation Systemsijtsrd
Human expressions can convey a great deal of information. We cannot learn every language in the world, but we can decipher the majority of human expressions. The state of a users conduct in various settings and scenarios can be inferred from their facial expressions. Through various human computer interface and programming approaches, facial expression can be digitized. Face detection, feature extraction, and kind of expression determination are all parts of the facial expression perception process. Verbal and non verbal forms of communication are both possible. Through their emotions, people can communicate nonverbally Weve given a broad summary of the various facial expression perception processes in the literature in this article. Jyoti | Neeraj Chawaria | Ekta "A Study on Face Expression Observation Systems" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50450.pdf Paper URL: https://www.ijtsrd.com/computer-science/computer-graphics/50450/a-study-on-face-expression-observation-systems/jyoti
This document summarizes a capstone report that describes the creation of a facial recognition system using a convolutional neural network to identify emotions and gender from images of faces. The system achieves 93% accuracy for emotion recognition and nearly 90% accuracy for gender detection. It analyzes grayscale images of faces that are 48 by 48 pixels in size to identify these attributes. The system is deployed through a web application built with Flask to allow real-time analysis and interpretation of video feeds or images. The goal is to demonstrate how this technology can be applied in business contexts like customer relationship management systems to enhance customer experiences.
Facial emotion recognition using deep learning detector and classifier IJECEIAES
Numerous research works have been put forward over the years to advance the field of facial expression recognition which until today, is still considered a challenging task. The selection of image color space and the use of facial alignment as preprocessing steps may collectively pose a significant impact on the accuracy and computational cost of facial emotion recognition, which is crucial to optimize the speed-accuracy trade-off. This paper proposed a deep learning-based facial emotion recognition pipeline that can be used to predict the emotion of detected face regions in video sequences. Five well-known state-of-the-art convolutional neural network architectures are used for training the emotion classifier to identify the network architecture which gives the best speed-accuracy trade-off. Two distinct facial emotion training datasets are prepared to investigate the effect of image color space and facial alignment on the performance of facial emotion recognition. Experimental results show that training a facial expression recognition model with grayscale-aligned facial images is preferable as it offers better recognition rates with lower detection latency. The lightweight MobileNet_v1 is identified as the best-performing model with WM=0.75 and RM=160 as its hyperparameters, achieving an overall accuracy of 86.42% on the testing video dataset.
The objective of emotion recognition is
identifying emotions of a human. The emotion can be
captured either from face or from verbal communication. In
this work we focus on identifying human emotion from
facial expressions. Facial emotion recognition is one of the
useful task and can be used as a base for many real-time
applications.
STUDENT TEACHER INTERACTION ANALYSIS WITH EMOTION RECOGNITION FROM VIDEO AND ...IRJET Journal
1) The document discusses a study analyzing student-teacher interactions through emotion recognition from video and audio inputs.
2) It aims to understand how students' emotional states relate to comprehension by defining facial behaviors associated with emotions.
3) The study examines the usefulness of recognizing facial expressions and voice between a teacher and student in a classroom setting. It analyzes whether students' facial expressions convey emotions in relation to understanding.
Development of video-based emotion recognition using deep learning with Googl...TELKOMNIKA JOURNAL
Emotion recognition using images, videos, or speech as input is considered as a hot topic in the field of research over some years. With the introduction of deep learning techniques, e.g., convolutional neural networks (CNN), applied in emotion recognition, has produced promising results. Human facial expressions are considered as critical components in understanding one's emotions. This paper sheds light on recognizing the emotions using deep learning techniques from the videos. The methodology of the recognition process, along with its description, is provided in this paper. Some of the video-based datasets used in many scholarly works are also examined. Results obtained from different emotion recognition models are presented along with their performance parameters. An experiment was carried out on the fer2013 dataset in Google Colab for depression detection, which came out to be 97% accurate on the training set and 57.4% accurate on the testing set.
This document discusses face recognition technology. It begins with an abstract stating that face recognition is the identification of humans by unique facial characteristics. It then discusses how face recognition works by identifying distinguishing facial features from images and comparing them to stored data. The document then provides an introduction to biometrics and how face recognition can be used for applications like criminal identification. It describes different face recognition algorithms and provides summaries of several research papers on face recognition techniques.
IRJET- Prediction of Human Facial Expression using Deep LearningIRJET Journal
This document discusses predicting human facial expressions using deep learning. It begins with an introduction to emotion recognition, facial emotion recognition, and deep learning techniques. It then reviews related literature on facial expression recognition using methods like CNNs, active appearance models, and analyzing facial features. The proposed system and methodology are described, including face registration, feature extraction focusing on action units, and using classifiers like KNN, MLP, and SVM to classify emotions based on these features. The goal is to recognize seven basic emotions (neutral, joy, sadness, surprise, anger, fear, disgust) in still images using deep learning techniques.
A Study on Face Expression Observation Systemsijtsrd
Human expressions can convey a great deal of information. We cannot learn every language in the world, but we can decipher the majority of human expressions. The state of a users conduct in various settings and scenarios can be inferred from their facial expressions. Through various human computer interface and programming approaches, facial expression can be digitized. Face detection, feature extraction, and kind of expression determination are all parts of the facial expression perception process. Verbal and non verbal forms of communication are both possible. Through their emotions, people can communicate nonverbally Weve given a broad summary of the various facial expression perception processes in the literature in this article. Jyoti | Neeraj Chawaria | Ekta "A Study on Face Expression Observation Systems" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50450.pdf Paper URL: https://www.ijtsrd.com/computer-science/computer-graphics/50450/a-study-on-face-expression-observation-systems/jyoti
This document summarizes a capstone report that describes the creation of a facial recognition system using a convolutional neural network to identify emotions and gender from images of faces. The system achieves 93% accuracy for emotion recognition and nearly 90% accuracy for gender detection. It analyzes grayscale images of faces that are 48 by 48 pixels in size to identify these attributes. The system is deployed through a web application built with Flask to allow real-time analysis and interpretation of video feeds or images. The goal is to demonstrate how this technology can be applied in business contexts like customer relationship management systems to enhance customer experiences.
Facial emotion recognition using deep learning detector and classifier IJECEIAES
Numerous research works have been put forward over the years to advance the field of facial expression recognition which until today, is still considered a challenging task. The selection of image color space and the use of facial alignment as preprocessing steps may collectively pose a significant impact on the accuracy and computational cost of facial emotion recognition, which is crucial to optimize the speed-accuracy trade-off. This paper proposed a deep learning-based facial emotion recognition pipeline that can be used to predict the emotion of detected face regions in video sequences. Five well-known state-of-the-art convolutional neural network architectures are used for training the emotion classifier to identify the network architecture which gives the best speed-accuracy trade-off. Two distinct facial emotion training datasets are prepared to investigate the effect of image color space and facial alignment on the performance of facial emotion recognition. Experimental results show that training a facial expression recognition model with grayscale-aligned facial images is preferable as it offers better recognition rates with lower detection latency. The lightweight MobileNet_v1 is identified as the best-performing model with WM=0.75 and RM=160 as its hyperparameters, achieving an overall accuracy of 86.42% on the testing video dataset.
The objective of emotion recognition is
identifying emotions of a human. The emotion can be
captured either from face or from verbal communication. In
this work we focus on identifying human emotion from
facial expressions. Facial emotion recognition is one of the
useful task and can be used as a base for many real-time
applications.
STUDENT TEACHER INTERACTION ANALYSIS WITH EMOTION RECOGNITION FROM VIDEO AND ...IRJET Journal
1) The document discusses a study analyzing student-teacher interactions through emotion recognition from video and audio inputs.
2) It aims to understand how students' emotional states relate to comprehension by defining facial behaviors associated with emotions.
3) The study examines the usefulness of recognizing facial expressions and voice between a teacher and student in a classroom setting. It analyzes whether students' facial expressions convey emotions in relation to understanding.
Development of video-based emotion recognition using deep learning with Googl...TELKOMNIKA JOURNAL
Emotion recognition using images, videos, or speech as input is considered as a hot topic in the field of research over some years. With the introduction of deep learning techniques, e.g., convolutional neural networks (CNN), applied in emotion recognition, has produced promising results. Human facial expressions are considered as critical components in understanding one's emotions. This paper sheds light on recognizing the emotions using deep learning techniques from the videos. The methodology of the recognition process, along with its description, is provided in this paper. Some of the video-based datasets used in many scholarly works are also examined. Results obtained from different emotion recognition models are presented along with their performance parameters. An experiment was carried out on the fer2013 dataset in Google Colab for depression detection, which came out to be 97% accurate on the training set and 57.4% accurate on the testing set.
This document discusses face recognition technology. It begins with an abstract stating that face recognition is the identification of humans by unique facial characteristics. It then discusses how face recognition works by identifying distinguishing facial features from images and comparing them to stored data. The document then provides an introduction to biometrics and how face recognition can be used for applications like criminal identification. It describes different face recognition algorithms and provides summaries of several research papers on face recognition techniques.
The thesis discusses detecting facial expressions and key facial coordinates using deep learning techniques. The objectives are to detect key facial coordinates and recognize seven universal facial expressions with high accuracy. The methodology uses the FER2013 dataset and a key facial points dataset, applying preprocessing like resizing and data augmentation. A residual network model is trained on this data, achieving accuracy scores that outperform previous state-of-the-art models in recognizing both facial expressions and key coordinates.
Final Year Project - Enhancing Virtual Learning through Emotional Agents (Doc...Sana Nasar
This document discusses approaches for face and emotion detection in virtual learning environments. It begins by explaining that face and emotion detection can improve human-computer interaction for virtual learning. It then discusses the need for face detection to improve efficiency of face recognition systems. Next, it describes how emotion detection is important for understanding human communication and states of mind. The document proceeds to explain existing approaches for face detection, including feature-based methods using texture and skin color, template matching methods, and appearance-based methods. It also outlines existing approaches for emotion detection using genetic algorithms, neural networks, and feature point extraction. Template matching is discussed as a technique for both face and emotion detection.
PRE-PROCESSING TECHNIQUES FOR FACIAL EMOTION RECOGNITION SYSTEMIAEME Publication
Humans share a universal and fundamental set of emotions which are exhibited through consistent facial expressions. Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature extraction and classification technique for emotion recognition is still an open problem. Image pre-processing and normalization is significant part of face recognition systems. Changes in lighting conditions produces dramatically decrease of recognition performance. In this paper, the image pre-processing techniques like K-Nearest Neighbor, Cultural Algorithm and Genetic Algorithm are used to remove the noise in the facial image for enhancing the emotion recognition. The performance of the preprocessing techniques are evaluated with various performance metrics.
ENHANCING THE HUMAN EMOTION RECOGNITION WITH FEATURE EXTRACTION TECHNIQUESIAEME Publication
This document discusses techniques for enhancing human emotion recognition through feature extraction. It begins by introducing the importance of recognizing human emotions through facial expressions. It then reviews related work applying techniques like deep learning, feature selection, and curriculum learning to emotion recognition. The document goes on to describe optimization techniques like ant colony optimization, particle swarm optimization, and genetic algorithms that can be used for feature extraction. It suggests these techniques may improve emotion recognition performance by selecting important features.
This document summarizes a research paper on image-based static facial expression recognition using multiple deep convolutional neural networks. The researchers used an ensemble of face detectors to locate faces in images, then classified the facial expressions using an ensemble of CNN models pre-trained on a larger dataset and fine-tuned on the SFEW 2.0 dataset. They proposed two methods for learning the ensemble weights of the CNN models by minimizing log likelihood or hinge loss. Their method achieved state-of-the-art results on the FER dataset and 61.29% accuracy on the SFEW 2.0 test set, significantly above the baseline.
ANALYSING SPEECH EMOTION USING NEURAL NETWORK ALGORITHMIRJET Journal
The document proposes a deep learning model to identify speech emotion using neural network algorithms. It discusses using convolutional neural networks and LSTM to extract acoustic features from speech signals and accurately identify emotional states. The model aims to improve the accuracy and robustness of speech emotion recognition systems, with applications in healthcare, customer service, and human-computer interaction.
Efficient Facial Expression and Face Recognition using Ranking MethodIJERA Editor
Expression detection is useful as a non-invasive method of lie detection and behaviour prediction. However, these facial expressions may be difficult to detect to the untrained eye. In this paper we implements facial expression recognition techniques using Ranking Method. The human face plays an important role in our social interaction, conveying people's identity. Using human face as a key to security, the biometrics face recognition technology has received significant attention in the past several years. Experiments are performed using standard database like surprise, sad and happiness. The universally accepted three principal emotions to be recognized are: surprise, sad and happiness along with neutral.
Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Cl...BIJIAM Journal
Facial expression recognition is a vital research topic in most fields ranging from artificial intelligence and gaming to human–computer interaction (HCI) and psychology. This paper proposes a hybrid model for facial expression recognition, which comprises a deep convolutional neural network (DCNN) and a Haar Cascade deep learning architecture. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The DCNN employed in this research has more convolutional layers, ReLU activation functions, and multiple kernels to enhance filtering depth and facial feature extraction. In addition, a Haar Cascade model was also mutually used to detect facial features in real-time images and video frames. Grayscale images from the Kaggle repository (FER-2013) and then exploited graphics processing unit (GPU) computation to expedite the training and validation process. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. The experimental results show a significantly improved classification performance compared to state-of-the-art (SoTA) experiments and research. Also, compared to other conventional models, this paper validates that the proposed architecture is superior in classification performance with an improvement of up to 6%, totaling up to 70% accuracy, and with less execution time of 2,098.8 s.
Face recognition for presence system by using residual networks-50 architectu...IJECEIAES
Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
Analysis on techniques used to recognize and identifying the Human emotions IJECEIAES
Facial expression is a major area for non-verbal language in day to day life communication. As the statistical analysis shows only 7 percent of the message in communication was covered in verbal communication while 55 percent transmitted by facial expression. Emotional expression has been a research subject of physiology since Darwin’s work on emotional expression in the 19th century. According to Psychological theory the classification of human emotion is classified majorly into six emotions: happiness, fear, anger, surprise, disgust, and sadness. Facial expressions which involve the emotions and the nature of speech play a foremost role in expressing these emotions. Thereafter, researchers developed a system based on Anatomic of face named Facial Action Coding System (FACS) in 1970. Ever since the development of FACS there is a rapid progress in the domain of emotion recognition. This work is intended to give a thorough comparative analysis of the various techniques and methods that were applied to recognize and identify human emotions. This analysis results will help to identify proper and suitable techniques, algorithms and the methodologies for future research directions. In this paper extensive analysis on various recognition techniques used to identify the complexity in recognizing the facial expression is presented.
This document summarizes several research papers on human face recognition using feature extraction and measurements. It discusses using face recognition for applications like surveillance, access control, and banking validation. Key steps in face recognition systems include extracting features from captured images, comparing them to known images in a training database, and identifying errors like false acceptance and false rejection rates. Methods discussed for feature extraction and dimensionality reduction include Linear Discriminant Analysis and Principal Component Analysis. The document also examines factors that affect face recognition performance like illumination changes, aging, and expressions. Quantifying uncertainty in face recognition algorithms is identified as important for evaluating system performance.
IRJET- An Innovative Approach for Interviewer to Judge State of Mind of an In...IRJET Journal
This document presents a proposed system to analyze the state of mind of an interviewee during an interview using facial expression recognition and classification. The system would use Fisher Face algorithm to detect facial features from video frames and Naive Bayes classification to categorize the detected expressions as indicators of emotional states like happy, sad, angry etc. This automated analysis of facial expressions could provide feedback to improve the interview process and selection of candidates. The summarized system aims to identify an individual's state of mind during an interview through facial expression recognition using deep learning techniques.
Robust face recognition by applying partitioning around medoids over eigen fa...ijcsa
An unsupervised learning methodology for robust face recognition is proposed for enhancing invariance to
various changes in the face. The area of face recognition in spite of being the most unobtrusive biometric
modality of all has encountered challenges with high performance in uncontrolled environment owing to
frequently occurring, unavoidable variations in the face. These changes may be due to noise, outliers,
changing expressions, emotions, pose, illumination, facial distractions like makeup, spectacles, hair growth
etc. Methods for dealing with these variations have been developed in the past with different success.
However the cost and time efficiency play a crucial role in implementing any methodology in real world.
This paper presents a method to integrate the technique of Partitioning Around Medoids with Eigen Faces
and Fisher Faces to improve the efficiency of face recognition considerably. The system so designed has
higher resistance towards the impact of various changes in the face and performs well in terms of success
rate, cost involved and time complexity. The methodology can therefore be used in developing highly robust
face recognition systems for real time environment.
This document summarizes research on sign language recognition systems. It discusses previous work on image-based sign language recognition using approaches like colored gloves, geometric feature extraction, and orientation histograms. It then describes the proposed system, an Android application that uses hand gesture recognition with real-time text and speech conversion. Key steps include gesture extraction using background subtraction and blob detection, gesture matching, and text-to-speech conversion. The system allows users to define their own sign language database to facilitate communication across different sign languages.
The document describes a deep learning model called Deep-Emotion that uses an attentional convolutional network for facial expression recognition. The model focuses on important facial regions like the eyes and mouth that are most relevant to detecting emotions. The model achieves state-of-the-art accuracy on several facial expression datasets, outperforming previous methods. Visualization techniques show the model learns to focus on different facial regions for different emotions.
This document provides a synopsis for a project on emotion detection from facial expressions. It outlines the objectives to develop an automatic emotion detection system using machine learning algorithms to analyze facial expressions in video frames and compare them to a database to classify emotions. The technical details discuss using a facial tracker and extracting features to represent expressions. Classification algorithms like KNN, SVM, and voting will be used for recognition and mapping expressions to emotions. Future work may include 3D processing, speech recognition, and detecting micro-expressions.
CONFIDENCE LEVEL ESTIMATOR BASED ON FACIAL AND VOICE EXPRESSION RECOGNITION A...IRJET Journal
This document describes a proposed system to estimate a person's confidence level based on analyzing their facial expressions and voice during interactions. The system uses convolutional neural networks to classify emotions from facial images. It also analyzes voice samples to detect emotion based on pitch and tone. Both the facial and voice models output confidence levels. The system aims to overcome limitations of existing expression recognition systems and provide a more holistic confidence level reading during questions/answers to help evaluate students or examinees.
This document presents a facial expression recognition system that identifies and classifies seven basic expressions: happy, surprise, fear, disgust, sad, anger, and a neutral state. The system consists of four main parts: image acquisition, pre-processing, feature extraction, and classification. It was developed using both OpenCV and a web-based JavaScript approach. The system was tested on both real-time and pre-recorded video streams and can identify emotions in images and video input from a webcam in real-time. Evaluation showed the JavaScript implementation using a generalized dataset provided more accurate real-time predictions compared to the OpenCV approach.
Facial emoji recognition is a human computer interaction system. In recent times, automatic face recognition or facial expression recognition has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and similar fields. Facial emoji recognizer is an end user application which detects the expression of the person in the video being captured by the camera. The smiley relevant to the expression of the person in the video is shown on the screen which changes with the change in the expressions. Facial expressions are important in human communication and interactions. Also, they are used as an important tool in studies about behavior and in medical fields. Facial emoji recognizer provides a fast and practical approach for non meddlesome emotion detection. The purpose was to develop an intelligent system for facial based expression classification using CNN algorithm. Haar classifier is used for face detection and CNN algorithm is utilized for the expression detection and giving the emoticon relevant to the expression as the output. N. Swapna Goud | K. Revanth Reddy | G. Alekhya | G. S. Sucheta ""Facial Emoji Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23166.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23166/facial-emoji-recognition/n-swapna-goud
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Contenu connexe
Similaire à Facial emotion recognition using enhanced multi-verse optimizer method
The thesis discusses detecting facial expressions and key facial coordinates using deep learning techniques. The objectives are to detect key facial coordinates and recognize seven universal facial expressions with high accuracy. The methodology uses the FER2013 dataset and a key facial points dataset, applying preprocessing like resizing and data augmentation. A residual network model is trained on this data, achieving accuracy scores that outperform previous state-of-the-art models in recognizing both facial expressions and key coordinates.
Final Year Project - Enhancing Virtual Learning through Emotional Agents (Doc...Sana Nasar
This document discusses approaches for face and emotion detection in virtual learning environments. It begins by explaining that face and emotion detection can improve human-computer interaction for virtual learning. It then discusses the need for face detection to improve efficiency of face recognition systems. Next, it describes how emotion detection is important for understanding human communication and states of mind. The document proceeds to explain existing approaches for face detection, including feature-based methods using texture and skin color, template matching methods, and appearance-based methods. It also outlines existing approaches for emotion detection using genetic algorithms, neural networks, and feature point extraction. Template matching is discussed as a technique for both face and emotion detection.
PRE-PROCESSING TECHNIQUES FOR FACIAL EMOTION RECOGNITION SYSTEMIAEME Publication
Humans share a universal and fundamental set of emotions which are exhibited through consistent facial expressions. Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature extraction and classification technique for emotion recognition is still an open problem. Image pre-processing and normalization is significant part of face recognition systems. Changes in lighting conditions produces dramatically decrease of recognition performance. In this paper, the image pre-processing techniques like K-Nearest Neighbor, Cultural Algorithm and Genetic Algorithm are used to remove the noise in the facial image for enhancing the emotion recognition. The performance of the preprocessing techniques are evaluated with various performance metrics.
ENHANCING THE HUMAN EMOTION RECOGNITION WITH FEATURE EXTRACTION TECHNIQUESIAEME Publication
This document discusses techniques for enhancing human emotion recognition through feature extraction. It begins by introducing the importance of recognizing human emotions through facial expressions. It then reviews related work applying techniques like deep learning, feature selection, and curriculum learning to emotion recognition. The document goes on to describe optimization techniques like ant colony optimization, particle swarm optimization, and genetic algorithms that can be used for feature extraction. It suggests these techniques may improve emotion recognition performance by selecting important features.
This document summarizes a research paper on image-based static facial expression recognition using multiple deep convolutional neural networks. The researchers used an ensemble of face detectors to locate faces in images, then classified the facial expressions using an ensemble of CNN models pre-trained on a larger dataset and fine-tuned on the SFEW 2.0 dataset. They proposed two methods for learning the ensemble weights of the CNN models by minimizing log likelihood or hinge loss. Their method achieved state-of-the-art results on the FER dataset and 61.29% accuracy on the SFEW 2.0 test set, significantly above the baseline.
ANALYSING SPEECH EMOTION USING NEURAL NETWORK ALGORITHMIRJET Journal
The document proposes a deep learning model to identify speech emotion using neural network algorithms. It discusses using convolutional neural networks and LSTM to extract acoustic features from speech signals and accurately identify emotional states. The model aims to improve the accuracy and robustness of speech emotion recognition systems, with applications in healthcare, customer service, and human-computer interaction.
Efficient Facial Expression and Face Recognition using Ranking MethodIJERA Editor
Expression detection is useful as a non-invasive method of lie detection and behaviour prediction. However, these facial expressions may be difficult to detect to the untrained eye. In this paper we implements facial expression recognition techniques using Ranking Method. The human face plays an important role in our social interaction, conveying people's identity. Using human face as a key to security, the biometrics face recognition technology has received significant attention in the past several years. Experiments are performed using standard database like surprise, sad and happiness. The universally accepted three principal emotions to be recognized are: surprise, sad and happiness along with neutral.
Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Cl...BIJIAM Journal
Facial expression recognition is a vital research topic in most fields ranging from artificial intelligence and gaming to human–computer interaction (HCI) and psychology. This paper proposes a hybrid model for facial expression recognition, which comprises a deep convolutional neural network (DCNN) and a Haar Cascade deep learning architecture. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The DCNN employed in this research has more convolutional layers, ReLU activation functions, and multiple kernels to enhance filtering depth and facial feature extraction. In addition, a Haar Cascade model was also mutually used to detect facial features in real-time images and video frames. Grayscale images from the Kaggle repository (FER-2013) and then exploited graphics processing unit (GPU) computation to expedite the training and validation process. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. The experimental results show a significantly improved classification performance compared to state-of-the-art (SoTA) experiments and research. Also, compared to other conventional models, this paper validates that the proposed architecture is superior in classification performance with an improvement of up to 6%, totaling up to 70% accuracy, and with less execution time of 2,098.8 s.
Face recognition for presence system by using residual networks-50 architectu...IJECEIAES
Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
Analysis on techniques used to recognize and identifying the Human emotions IJECEIAES
Facial expression is a major area for non-verbal language in day to day life communication. As the statistical analysis shows only 7 percent of the message in communication was covered in verbal communication while 55 percent transmitted by facial expression. Emotional expression has been a research subject of physiology since Darwin’s work on emotional expression in the 19th century. According to Psychological theory the classification of human emotion is classified majorly into six emotions: happiness, fear, anger, surprise, disgust, and sadness. Facial expressions which involve the emotions and the nature of speech play a foremost role in expressing these emotions. Thereafter, researchers developed a system based on Anatomic of face named Facial Action Coding System (FACS) in 1970. Ever since the development of FACS there is a rapid progress in the domain of emotion recognition. This work is intended to give a thorough comparative analysis of the various techniques and methods that were applied to recognize and identify human emotions. This analysis results will help to identify proper and suitable techniques, algorithms and the methodologies for future research directions. In this paper extensive analysis on various recognition techniques used to identify the complexity in recognizing the facial expression is presented.
This document summarizes several research papers on human face recognition using feature extraction and measurements. It discusses using face recognition for applications like surveillance, access control, and banking validation. Key steps in face recognition systems include extracting features from captured images, comparing them to known images in a training database, and identifying errors like false acceptance and false rejection rates. Methods discussed for feature extraction and dimensionality reduction include Linear Discriminant Analysis and Principal Component Analysis. The document also examines factors that affect face recognition performance like illumination changes, aging, and expressions. Quantifying uncertainty in face recognition algorithms is identified as important for evaluating system performance.
IRJET- An Innovative Approach for Interviewer to Judge State of Mind of an In...IRJET Journal
This document presents a proposed system to analyze the state of mind of an interviewee during an interview using facial expression recognition and classification. The system would use Fisher Face algorithm to detect facial features from video frames and Naive Bayes classification to categorize the detected expressions as indicators of emotional states like happy, sad, angry etc. This automated analysis of facial expressions could provide feedback to improve the interview process and selection of candidates. The summarized system aims to identify an individual's state of mind during an interview through facial expression recognition using deep learning techniques.
Robust face recognition by applying partitioning around medoids over eigen fa...ijcsa
An unsupervised learning methodology for robust face recognition is proposed for enhancing invariance to
various changes in the face. The area of face recognition in spite of being the most unobtrusive biometric
modality of all has encountered challenges with high performance in uncontrolled environment owing to
frequently occurring, unavoidable variations in the face. These changes may be due to noise, outliers,
changing expressions, emotions, pose, illumination, facial distractions like makeup, spectacles, hair growth
etc. Methods for dealing with these variations have been developed in the past with different success.
However the cost and time efficiency play a crucial role in implementing any methodology in real world.
This paper presents a method to integrate the technique of Partitioning Around Medoids with Eigen Faces
and Fisher Faces to improve the efficiency of face recognition considerably. The system so designed has
higher resistance towards the impact of various changes in the face and performs well in terms of success
rate, cost involved and time complexity. The methodology can therefore be used in developing highly robust
face recognition systems for real time environment.
This document summarizes research on sign language recognition systems. It discusses previous work on image-based sign language recognition using approaches like colored gloves, geometric feature extraction, and orientation histograms. It then describes the proposed system, an Android application that uses hand gesture recognition with real-time text and speech conversion. Key steps include gesture extraction using background subtraction and blob detection, gesture matching, and text-to-speech conversion. The system allows users to define their own sign language database to facilitate communication across different sign languages.
The document describes a deep learning model called Deep-Emotion that uses an attentional convolutional network for facial expression recognition. The model focuses on important facial regions like the eyes and mouth that are most relevant to detecting emotions. The model achieves state-of-the-art accuracy on several facial expression datasets, outperforming previous methods. Visualization techniques show the model learns to focus on different facial regions for different emotions.
This document provides a synopsis for a project on emotion detection from facial expressions. It outlines the objectives to develop an automatic emotion detection system using machine learning algorithms to analyze facial expressions in video frames and compare them to a database to classify emotions. The technical details discuss using a facial tracker and extracting features to represent expressions. Classification algorithms like KNN, SVM, and voting will be used for recognition and mapping expressions to emotions. Future work may include 3D processing, speech recognition, and detecting micro-expressions.
CONFIDENCE LEVEL ESTIMATOR BASED ON FACIAL AND VOICE EXPRESSION RECOGNITION A...IRJET Journal
This document describes a proposed system to estimate a person's confidence level based on analyzing their facial expressions and voice during interactions. The system uses convolutional neural networks to classify emotions from facial images. It also analyzes voice samples to detect emotion based on pitch and tone. Both the facial and voice models output confidence levels. The system aims to overcome limitations of existing expression recognition systems and provide a more holistic confidence level reading during questions/answers to help evaluate students or examinees.
This document presents a facial expression recognition system that identifies and classifies seven basic expressions: happy, surprise, fear, disgust, sad, anger, and a neutral state. The system consists of four main parts: image acquisition, pre-processing, feature extraction, and classification. It was developed using both OpenCV and a web-based JavaScript approach. The system was tested on both real-time and pre-recorded video streams and can identify emotions in images and video input from a webcam in real-time. Evaluation showed the JavaScript implementation using a generalized dataset provided more accurate real-time predictions compared to the OpenCV approach.
Facial emoji recognition is a human computer interaction system. In recent times, automatic face recognition or facial expression recognition has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and similar fields. Facial emoji recognizer is an end user application which detects the expression of the person in the video being captured by the camera. The smiley relevant to the expression of the person in the video is shown on the screen which changes with the change in the expressions. Facial expressions are important in human communication and interactions. Also, they are used as an important tool in studies about behavior and in medical fields. Facial emoji recognizer provides a fast and practical approach for non meddlesome emotion detection. The purpose was to develop an intelligent system for facial based expression classification using CNN algorithm. Haar classifier is used for face detection and CNN algorithm is utilized for the expression detection and giving the emoticon relevant to the expression as the output. N. Swapna Goud | K. Revanth Reddy | G. Alekhya | G. S. Sucheta ""Facial Emoji Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23166.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23166/facial-emoji-recognition/n-swapna-goud
Similaire à Facial emotion recognition using enhanced multi-verse optimizer method (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
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Facial emotion recognition using enhanced multi-verse optimizer method
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 2, April 2024, pp. 1519~1529
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i2.pp1519-1529 1519
Journal homepage: http://ijece.iaescore.com
Facial emotion recognition using enhanced multi-verse
optimizer method
Ravi Gummula, Vinothkumar Arumugam, Abilasha Aranganathan
Department of Electronics and Communication Engineering, Dr. M.G.R. Educational and Research Institute, Chennai, India
Article Info ABSTRACT
Article history:
Received Jul 6, 2023
Revised Sep 29, 2023
Accepted Nov 17, 2023
In recent years, facial emotion recognition has gained significant
improvement and attention. This technology utilizes advanced algorithms to
analyze facial expressions, enabling computers to detect and interpret human
emotions accurately. Its applications span over a wide range of fields, from
improving customer service through sentiment analysis, to enhancing mental
health support by monitoring emotional states. However, there are several
challenges in facial emotion recognition, including variability in individual
expressions, cultural differences in emotion display, and privacy concerns
related to data collection and usage. Lighting conditions, occlusions, and the
need for diverse datasets also impacts accuracy. To solve these issues, an
enhanced multi-verse optimizer (EMVO) technique is proposed to improve
the efficiency of recognizing emotions. Moreover, EMVO is used to
improve the convergence speed, exploration-exploitation balance, solution
quality, and the applicability in different types of optimization problems.
Two datasets were used to collect the data, namely YouTube and surrey
audio-visual expressed emotion (SAVEE) datasets. Then, the classification
is done using the convolutional neural networks (CNN) to improve the
performance of emotion recognition. When compared to the existing
methods shuffled frog leaping algorithm-incremental wrapper-based subset
selection (SFLA-IWSS), hierarchical deep neural network (H-DNN) and
unique preference learning (UPL), the proposed method achieved better
accuracies, measured at 98.65% and 98.76% on the YouTube and SAVEE
datasets, respectively.
Keywords:
Enhanced multi-verse optimizer
Machine learning
Surrey audio-visual expressed
emotion
Text recognition
YouTube
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ravi Gummula
Department of Electronics and Communication Engineering, Dr. M.G.R. Educational and Research Institute
Chennai, India
Email: ravi.gummula@gmail.com
1. INTRODUCTION
Facial expressions serve as the most instinctive form of conveying human emotions and play a vital
role in the overall system of expressing emotions. In everyday communication, individuals can quickly
communicate their underlying feelings and intentions through facial expressions [1], [2]. Face recognition is
always considered an attractive area of research among researchers even today, due to many of its internet of
things (IoT) applications [3]. Most popular techniques are now suited for sustaining data utility in terms of
pre-defined criteria such as structural similarity or face form, which suffers the censure of insufficient
variation and adaptableness [4], [5]. Several techniques use the posture estimation method for single-picture
face recognition, but only a minority integrate the pose estimation method in image set-based face
identification [6], [7]. The existing studies focused on intended emotion labels, and few also focused on
seeming labels for better results to increase emotion recognition accuracy [8]. Multimodal fusion and feature
2. ISSN: 2088-8708
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selection are the two most difficulties in affective computing and multimodal sentiment analysis [9]. The
characteristics retrieved from separate modalities are multidimensional, and many of them are redundant and
unnecessary [10], [11]. A previously conducted research focused on the reported facial expression
classifications of displeasure/anger, sad/unhappy, smiling/happy, afraid, and surprised/astonished [12]. In
psychotic syndromes, recognition of positive expressions (happy) is intact, but recognition of negative
expressions (fear, anger, disgust, and sadness) is impaired for both positive and negative emotions [13].
Another research focused on overcoming the problems of blurred, low-resolution and noisy images for face
detection recognition systems by employing an enhanced multi-verse optimizer (EMVO) technique [14],
[15]. Facial emotion recognition is employed for its versatility in various applications. It enhances human-
computer interaction, aids mental health monitoring, and improves customer service by gauging emotions
[16], [17]. Additionally, it offers valuable insights in fields like market research and advertising, making it a
valuable tool for understanding and responding to human emotions [18]. However, while recognizing a face,
there are certain challenges such as refined expressions, ambiguous emotions, individual differences, and
context dependency. Facial emotion recognition proves to be difficult at times, due to insufficient data and
bias issues, making it a continually evolving research. [19], [20]. Hence, this research utilizes EMVO feature
selection method to further improve the accuracy in facial emotion recognition.
Kothuri and Rajalakshmi [21] discovered a shuffled frog leaping algorithm-incremental wrapper-
based subset selection (SFLA-IWSS) implementation, to enhance emotion detection. The suggested system
analyzed the user's emotions using audio, video, text elements and music recommendations to the
users/operators. However, the SFLA had limited adaptability towards the dynamic changing nature of the
emotion recognition tasks, thus, modifications were required to enhance the algorithm’s adaptability under
such scenarios. Singh et al. [22] developed a hierarchical deep learning-based technique to collect context-
dependent emotional elements from the text of both multimodal and unimodal SER systems. Combination of
words, spectral information and voice quality (VQ) were used by both global and local level audio
descriptors to represent how humans perceive emotion. However, the hierarchical deep learning technique
required large amounts of labelled training data to generalize the specific domains. Lei and Cao [23] created
novel preference learning algorithms for multimodal emotion recognition that overcame the disparity
between alleged labels on different modalities. However, preference learning algorithms often require
labelled data in the form of recognition. Acquiring such data is challenging and time-consuming, especially
when the number of data is large. Singh et al. [24] designed a two-dimensional convolutional neural network
(CNN) to identify the most effective features for the task. However, the suggested CNN-based method
encountered difficulties in capturing the essential temporal dynamics required for precise emotion
recognition. Mao et al. [25] implemented an improved CNN called SCAR-NET, to extract features from
speech signals, and perform classification. However, SCAR-NET significantly increased the computational
complexity of the network. As a result, an Improved multi-verse optimizer is presented in this research to
increase the features of emotion identification. As compared to existing methods in emotion recognition, the
suggested method achieved superior performance in accuracy. However, the Facial emotion recognition is a
challenging task due to the vast amount of various known facial expressions, which are assessed under
varying lighting and orientations. So, the existing methods struggled to select optimal features and navigate
high-dimensional spaces effectively. Additionally, achieving perfect accuracy often requires a combination
of sophisticated feature extraction, model design, and robust data handling. The limitations of the existing
methods, coupled with dataset diversity and challenges in emotion recognition, makes it tougher to
accomplish better performances on datasets like YouTube and SAVEE. EMVO effectively explores and
exploits the solution space in facial emotion recognition, inspired by cosmological principles. Cosmology’s
multiverse theory uses diverse solution sets in different universes to explore facial emotion feature spaces.
Cosmological principles perform efficient exploration, leading to enhanced accuracy, by identifying optimal
feature sets for improved emotion recognition models. EMVO offers a more systematic search of features,
helping in optimal feature selection and model design. Its balanced approach to exploration and exploitation
enhances accuracy by potentially finding more discriminative features. EMVO's ability to efficiently navigate
high-dimensional feature space, addresses the limitations in feature selection, leading to better accuracy in
facial emotion recognition compared to existing methods.
The contribution of the research are as follows; i) In this research, an enhanced multi-verse
optimizer (EMVO) approach is developed for feature selection method to improve the accuracy of facial
emotion recognition, ii) EMVO is crucial for facial emotion recognition due to its ability to efficiently
explore high-dimensional feature spaces and is therefore used to further improve the accuracy in the facial
emotion challenging task, iii) EMVO offers an advantage in emotion recognition by enabling faster and more
accurate assessment of facial expressions, voice tonality, and text sentiment. This proves to be useful in
applications like customer sentiment analysis, and more precisely helps in such emotion monitoring
techniques. The rest of this paper is organized as follows: section 2 illustrates the proposed method. Section 3
3. Int J Elec & Comp Eng ISSN: 2088-8708
Facial emotion recognition using enhanced multi-verse optimizer method (Ravi Gummula)
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denotes the suggested enhanced multi-verse optimization method. The implementation details and its results
are demonstrated in section 4. The conclusion of this research is given in section 5.
2. PROPOSED METHOD
In this section, the proposed multi-modal facial expression recognition and classification is analyzed.
The proposed method is implemented and simulated using MATLAB R2020b, with the system configuration
of i7 processor, 16 GB RAM and Windows 10 OS. The data collected from the datasets undergo pre-
processing, feature selection, feature extraction and classification, before giving it to the proposed method, as
briefly explained below. The flow diagram of the proposed method is shown in Figure 1.
Figure 1. Flow diagram of the suggested techniques
2.1. Data collection
In this research, two datasets are used to collect the data, namely YouTube and surrey audio-visual
expressed emotion (SAVEE) datasets [21], which are used to evaluate the suggested method’s performance.
These datasets are used to create automated emotion identification systems, that support image, video, text
and audio. Figure 2 illustrates sample emotions within distinct classes in the SAVEE and YouTube dataset.
Output 1:
Class 1 Class 2 Class 3
Output 2:
Class 4 Class 5 Class 6
Figure 2. Various emotions detection for the SAVEE dataset
4. ISSN: 2088-8708
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SAVEE dataset contains 480 total words, 3 standards, 2 emotion-specific, and ten general sentences.
The YouTube dataset is made up of 7 different emotions: disgust, anger, happiness, fear, surprise, sadness,
and neutral, all of which were recorded and assessed by 10 different individuals. After data collection from
these datasets, pre-processing is employed to restructure and prepare the data for further analysis or for
training the model. This involves various steps that addresses issues such as missing values, outliers, and
feature scaling, ensuring that the data is in a suitable format for subsequent tasks.
2.2. Pre-processing
After the data collection, normalization and punctuation removal are utilized to pre-process the
image data. In this research, data pre-processing is carried out in two ways, one is normalization [26] using
image/video and another is Punctuation removal using text. These are briefly explained below.
2.2.1. Normalization using image/video
Normalization is a preprocessing approach used to decrease differences in face photographs, such as
the degree of lighting, to obtain a better face image. The min-max normalization method is utilized to boost
image intensity, resulting in enhanced clarity of information and improved performance of classifiers. The
mathematical expression of normalization is illustrated in (1).
𝑥 =
𝑥− 𝑥𝑚𝑖𝑛
𝑥𝑚𝑎𝑥− 𝑥𝑚𝑖𝑛
(1)
where 𝑥𝑚𝑖𝑛 describes the image minimum intensity, 𝑥𝑚𝑎𝑥 describes the image maximum intensity, and 𝑥𝑛
describes the normalized image.
2.2.2. Punctuation removal using text
Punctuation signals in web social media include sentiment data which are the primary resource of
emotional data, and they have significant usefulness in addressing the absence of text content semantics.
After performing pre-processing on the data, feature extraction techniques are applied to the image, text,
video, and audio data, to extract relevant and meaningful information from each of these modalities. Once the
punctual removal is done, the relevant data is given as input to feature extraction stage, where bidirectional
encoder representations from transformers (BERT) model is used which is clearly described in the following
section.
2.3. Feature extraction using BERT model
In this research, the feature extraction is done using the BERT model for text that is fused into video
and audio features [27]. BERT is frequently chosen for facial emotion recognition due to its proven
performance, extensive adoption, and abundance of pretrained models, while robustly optimized BERT
pretraining approach (ROBERTa) requires enhancement in its adoption and validation for specific domain
applications like facial emotion recognition. Utilizing BERT in facial emotion recognition increases
comprehensibility of textual emotional context that often accompanies images or videos. BERT’s pre-trained
transformer model captures intricate language nuances, helping in understanding associated emotions within
textual descriptions or comments. This textual context, when fused with corresponding facial images or
videos, provides a more comprehensive understanding of the conveyed emotions. By considering both textual
and visual elements, the accuracy and depth of facial emotion recognition is significantly improved, enabling
a more nuanced and precise interpretation of emotional states and expressions depicted in the visual data. The
process of BERT using text feature extraction is briefly discussed below.
2.3.1. Text using latent Dirichlet allocation, inverse term frequency, and bag of words
Latent Dirichlet allocation (LDA) is a probabilistic generating approach that is given as random
mixes over latent themes. The mathematical expression of LDA is shown in (2). Moreover, inverse term
frequency, information extraction, text mining, and weighting factors are primarily employed to prevent
filtering terms in the text categorization and classification applications. BERT model is balanced by weights,
which helps to prevent certain words from being overused, as illustrated in (3).
( )
( )
( )
1
1 1
1
1
1
k
k
i
i
k
k
i i
p
= −
−
=
=
(2)
5. Int J Elec & Comp Eng ISSN: 2088-8708
Facial emotion recognition using enhanced multi-verse optimizer method (Ravi Gummula)
1523
𝛷𝑐,𝑣
𝑤𝑒𝑖𝑔ℎ𝑡
= 𝜂 × [
𝑆𝑁𝑐,𝑣
𝑆𝑁𝑐
× 𝑙𝑜𝑔 (
𝐶
𝐶𝑣
)] + 𝛾 (3)
where 𝛤 (𝑥) is the gamma function. Bag-of-words (BoW) model is an image representation technique for
image categorization and annotation tasks. To fuse text features with audio and video data, it typically
follows a multi-modal approach. First, it extracts text features using BERT as described above. Then, it
integrates them with audio and video features. The fused representation is then used for various multi-modal
tasks, such as sentiment analysis of video reviews, speech-to-text in videos, or video summarization,
depending on the specific application. The choice of fusion method depends on the nature of the data and the
goals of the task, allowing it to leverage the strengths of BERT’s text features alongside audio and video
information, for a more comprehensive analysis.
2.3.2. Image/video using CNN feature with AlexNet
CNN is a branch of deep learning which is widely used alongside AlexNet to improve data. It
includes five parts: a convolution layer, an input layer, an output layer, a pooling layer, and a full connection
layer. AlexNet contains 5 convolutional layers, 8 layers of neural networks, 3 pooling layers and 3 full
connection layers. The mathematical expression was shown in (4).
𝑓(𝑥) = {
𝑥, 𝑖𝑓 𝑦 > 0
0, 𝑖𝑓 𝑦 ≤ 0
} (4)
2.3.3. Using extracted acoustic features in audio
In this research, acoustic features are combined to increase the semantic data of the emotional
features in the audio [27]. After performing feature extraction on the data, the feature selection technique
called enhanced multi-verse optimizer (EMVO) is applied to identify and select the most significant features.
The mathematical expression of the extracted acoustic features is illustrated in (5).
2 2 2
2 1 2
1
1
.
1 n n
rms i
x x x
x x
n n
=
+ +
= =
(5)
3. ENHANCED MULTI-VERSE OPTIMIZER
In this research, an innovative approach is introduced for facial emotion recognition through the
utilization of the enhanced multi-verse optimizer (EMVO). The multi-verse optimization (MVO) faced
limitations in terms of exploration and convergence speed in complex optimization tasks. EMVO addresses
these issues by introducing multiple universes inspired by cosmological principles [28]. Cosmology’s
multiverse theory uses diverse solution sets in different universes to explore facial emotion feature spaces.
Cosmological principles perform efficient exploration, leading to enhanced accuracy, by identifying optimal
feature sets for improved emotion recognition models.
In the facial emotion recognition of EMVO, features are selected by utilizing a cosmology-inspired
optimization approach. In EMVO, the cosmology-inspired approach uses principles from the multiverse
theory to enhance the optimization process. Similarly, to multiple universes in cosmology, EMVO employs
multiple solutions in various universes to search and optimize facial emotion feature spaces. Each universe
represents a potential solution set, that explores diverse feature combinations and configurations. The
incorporation of cosmological principles helps guide the exploration strategy, allowing for more efficient and
effective search across the solution. By utilizing inspiration from the vastness of the cosmos, EMVO
significantly improves the accuracy and robustness of facial emotion recognition models by identifying
optimal feature sets. EMVO enhances exploration of the solution space by employing multiple universes and
optimizing these universes iteratively. This enhanced exploration helps in identifying the most discriminative
facial features. Additionally, the optimization process balances the exploration and exploitation, thereby
effectively fine-tuning and optimizing the feature selection procedure. By efficiently navigating the high-
dimensional feature space and optimization, EMVO improves the accuracy of facial emotion recognition by
selecting the most relevant and discriminative features from the emotional facial expressions. Hence, the
enhancements of the EMVO are as follows:
− EMVO incorporates adaptive control parameters. These parameters dynamically adjust during the
optimization process, ensuring the algorithm can fine-tune its exploration and exploitation strategies
based on the evolving problem state. This adaptability enables EMVO to navigate the solution space more
efficiently.
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− EMVO introduces a crossover operation inspired by genetic algorithms. This operation facilitates the
exchange of information among the universes, promoting diversity within the population of solutions, and
helping the algorithm escape local optima.
− The velocity update strategy in EMVO is modified to optimize the movement of universes within the
search space. Adjustments in velocities based on fitness values, guide the search towards promising
regions, which enhances the convergence speed and accuracy.
Feature selection involves the process of carefully choosing a subset of the most pertinent features
from a given set of original features, thereby eliminating redundant, irrelevant, or noisy features. To deal with
this problem, a wormhole existence probability (WEP) is considered in the algorithm for iterations that are
illustrated in (6).
𝑊𝐸𝑃 = {
0.2 + (0.8)
𝑡
𝑡𝑚𝑎𝑥
𝑡 <
𝑡𝑚𝑎𝑥
2
𝑤𝑚𝑖𝑛 + (𝑤𝑚𝑎𝑥 − 𝑤𝑚𝑖𝑛).
𝑡
𝑡𝑚𝑎𝑥
𝑡 ≥
𝑡𝑚𝑎𝑥
2
(6)
3.1. Fitness function for EMVO
The EMVO model, classifies into black holes, white holes, and wormholes. White and black holes
represent wormholes, while exploration and exploitation phases correspond to different stages. The fitness
function's mathematical expressions are defined in (7).
𝑥𝑖
𝑗
= {
𝑥𝑘
𝑗
𝑟1 < 𝑛𝑜𝑟𝑚𝑟(𝑈𝑖)
𝑥𝑖
𝑗
𝑟1 ≥ 𝑛𝑜𝑟𝑚𝑟(𝑈𝑖)
(7)
where 𝑛𝑜𝑟𝑚𝑟(𝑈𝑖) is the normalized inflation rate of 𝑖𝑡ℎ
universe and 𝑥𝑖
𝑗
is the 𝑗𝑡ℎ
variable. 𝑟1 is a random
number between [0,1]. 𝑥𝑘
𝑗
is the 𝑗𝑡ℎ
variable of 𝑘𝑡ℎ
universe. Following the feature selection process, the CNN
classification technique is employed to classify the data based on the selected features. The CNN algorithm
utilizes multiple support vector machines to handle complex and multi-class classification problems effectively.
3.2. Classification using convolutional neural network
Convolutional neural network (CNN) is a specialized neural network for image analysis, and
provides excellent results in facial emotion classification by extracting intricate features and recognizing
spatial patterns that are crucial for accurate emotion recognition [29]. Its hierarchical learning process
enables automated feature extraction, improving the model’s ability to interpret and classify emotions from
facial images [30]. When contrasted to a deep artificial neural network (ANN), CNNs have a lesser training
time. Therefore, CNNs have shown remarkable success in various computer vision tasks, including image
segmentation, identification, as well as classification, due to their ability to capture complex patterns and
spatial relationships. CNN segmentation as well as the classification system incorporates convolutional
layers, pooling layers, fully connected layers, drop-out layers. Figure 3 shows the basic architecture of CNN.
Figure 3. Basic architecture of CNN
In Figure 3, the first layer’s feature map is created by combining the input using six convolution
kernels. Every convolution kernel is 5×5 in size, with a stride of 1. Equations (8) and (9) are used to
calculate the feature map size:
𝑛𝑓 =
𝑛𝑖+2𝑝−𝑓
𝑠
(8)
7. Int J Elec & Comp Eng ISSN: 2088-8708
Facial emotion recognition using enhanced multi-verse optimizer method (Ravi Gummula)
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where, 𝑛𝑓 is feature map size, 𝑛𝑖 is input data size, 𝑝 is padding value, 𝑓 is kernels size, 𝑠 is stride value. The
basic formula of a convolution operation is given in (6):
𝑎𝑙
= 𝛿(𝑊𝑙
𝑎𝑙−1
+ 𝑏𝑙
) (9)
where, 𝑎𝑙
is 𝑙𝑡ℎ
convolution layer’s output, 𝑊𝑙
is 𝑙𝑡ℎ
convolution layer’s convolution kernel, 𝑎𝑙−1
is
(𝑙 − 1)𝑡ℎ
convolution layer output, 𝑏𝑙
is 𝑙𝑡ℎ
convolution layer’s bias, 𝛿 is 𝑙𝑡ℎ
convolution layer’s activation
function.
4. EXPERIMENTAL RESULTS
In this research, enhanced multi-verse optimizer is suggested to recognize facial expressions and to
increase the hyperparameters of CNN classification. The proposed method is implemented and simulated
using MATLAB R2020b with the system configuration of i7 processor, 16 GB RAM and Windows 10 OS.
The performance of the feature selection EMVO algorithm is evaluated using common performance
measures of sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and positive predictive
value (PPV). The mathematical equation for the aforementioned performance measures is given in Table 1.
Table 1. Mathematical equation of respective performance measures
Performance Measures Equations
Accuracy 𝑇𝑃 + 𝑇𝑁
𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁
Sensitivity 𝑇𝑃
𝑇𝑃 + 𝐹𝑁
Specificity 𝑇𝑁
𝑇𝑁 + 𝐹𝑃
MCC 𝑇𝑃 × 𝑇𝑁 − 𝐹𝑃 × 𝐹𝑁
√(𝑇𝑃 + 𝐹𝑃)(𝑇𝑃 + 𝐹𝑁)(𝑇𝑁 + 𝐹𝑃)(𝑇𝑁 + 𝐹𝑁)
PPV 𝑇𝑃
𝑇𝑃 + 𝐹𝑃
4.1. Performance analysis of multi-model features for YouTube and SAVEE dataset
Here, Table 2 represent the performance analysis of the enhanced multi-verse optimizer in YouTube
dataset with respect to various performance metrics. It shows that proposed method achieves a higher
accuracy of 98.65%, a sensitivity of 99.59%, a specificity of 98.76%, MCC of 98.87%, and a PPV of
98.01%. Whereas the text, audio, video, video+text, audio+text and video+Audio, attain their corresponding
accuracies at 92.90%, 94.00%, 96.86%, 95.98%, 89.76%, and 97.81%. Table 3 depicts that the hybrid
features achieve a higher accuracy of 98.76%, a sensitivity of 98.04%, a specificity of 97.04%, MCC of
98.54% and a PPV of 98.88%. Whereas the text, audio, video, video+text, audio+text and video+audio
accomplish their accuracies, respectively at 94.95%, 97.54%, 96.86%, 96.90%, 93.98%, and 97.24%.
Table 2. Performance analysis of the YouTube dataset
Multi-model features Accuracy (%) Sensitivity (%) Specificity (%) PPV (%) MCC (%)
Text 92.90 91.86 92.86 90.11 88.98
Audio 94.00 89.86 91.86 87.07 90.71
Video 96.86 93.43 95.86 94.24 95.37
Video+Text 95.98 96.86 97.86 94.73 93.98
Audio+Text 89.76 94.99 88.86 89.99 90.91
Video+_Audio 97.81 95.69 98.41 96.93 94.36
Hybrid_features(Text,Audio,Video) 98.65 99.59 98.76 98.01 98.87
Table 3. Performance analysis of SAVEE dataset
Multi-model features Accuracy (%) Sensitivity (%) Specificity (%) PPV (%) MCC (%)
Text 94.95 94.90 95.92 96.95 92.89
Audio 97.54 96.76 95.93 95.31 96.98
Video 96.86 97.81 95.54 94.76 96.88
Video+Text 96.90 95.60 93.97 93.94 94.80
Audio+Text 93.98 95.79 96.88 89.85 94.97
Video+Audio 97.24 96.86 95.98 98.33 95.86
Hybrid_features (Text, Audio, Video) 98.76 98.04 97.04 98.54 98.88
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4.2. Performance of feature selection methods for YouTube and SAVEE datasets
Here, Table 4 represents the performance analysis of feature selection methods namely particle
swarm optimization (PSO), ant colony optimization (ACO), and artificial bee optimization (ABC), in
YouTube dataset. Table 4 showed that the suggested method achieves a higher accuracy of 98.65%, the
sensitivity of 99.59%, a specificity of 98.76%, a PPV of 98.01% and MCC of 98.87%. Whereas the PSO,
ACO, and ABC achieved accuracy of 93.78%, 93.89%, and 90.64% respectively. Table 5 displays that the
proposed method produces a higher accuracy of 98.76%, sensitivity of 98.04%, specificity of 97.04%, PPV
of 98.54% and MCC of 98.87%. Whereas the PSO, ACO, and ABC achieved accuracy of 95.29%, 94.99%,
and 90.64% respectively.
Table 4. Performance analysis of feature selection methods for You tube dataset
Feature Selection methods Accuracy (%) Sensitivity (%) Specificity (%) PPV (%) MCC (%)
PSO 93.78 92.43 92.74 90.91 92.99
ACO 93.89 92.38 94.98 90.79 92.42
ABC 90.64 93.59 89.54 88.16 92.87
Proposed 98.65 99.59 98.76 98.01 98.87
Table 5. Performance analysis of feature selection methods for SAVEE dataset
Feature Selection methods Accuracy (%) Sensitivity (%) Specificity (%) PPV (%) MCC (%)
PSO 95.29 94.76 92.86 90.20 93.22
ACO 94.99 96.91 93.98 90.97 93.63
ABC 96.63 95.87 94.92 97.44 94.51
Proposed 98.76 98.04 97.04 98.54 98.88
4.3. Performance analysis of classification methods for YouTube and SAVEE dataset
Here, Table 6 represents the performance analysis of various classification methods such as
k-nearest neighbor (KNN), differential evaluation (DE) and random forest (RF), in YouTube dataset. After
analyzing Table 6, the inference is that the CNN accomplishes a higher accuracy of 98.65%, sensitivity of
99.59%, specificity of 98.76%, PPV of 98.01% and MCC of 98.87%. Whereas the KNN, RF, DE and CNN
achieve their respective accuracies at 91.76%, 91.38%, 95.74 and 98.65 %. Likewise, Table 7 potrays the
same analysis for the SAVEE dataset. In this, the CNN achieves a higher accuracy of 98.76%, a sensitivity of
98.04%, a specificity of 97.04%, a PPV of 98.54% and MCC of 98.88%. Whereas the KNN, RF, and DE
achieved respective accuracies at 92.90%, 90.90%, and 95.79%.
Table 6. Performance analysis of classification methods for YouTube dataset
Classifiers Accuracy (%) Sensitivity (%) Specificity (%) PPV (%) MCC (%)
KNN 91.76 90.87 92.86 89.11 88.88
RF 91.38 89.57 92.97 88.07 90.52
DE 95.74 94.86 96.98 95.98 93.98
CNN 98.65 99.59 98.76 98.01 98.87
Table 7. Performance analysis of classification methods for the SAVEE dataset
Classifier Accuracy (%) Sensitivity (%) Specificity (%) PPV (%) MCC (%)
KNN 92.90 93.88 94.43 95.89 88.18
RF 90.90 89.36 91.97 89.84 90.62
DE 95.79 93.68 96.98 94.68 97.37
CNN 98.76 98.04 97.04 98.54 98.88
4.4. Comparative analysis
This part, provides a comparative evaluation of the suggested enhanced multi-verse optimizer
(EMVO) model with existing models, which are the shuffled frog leaping algorithm incremental wrapped–
based subset selection (SFLA-IWSS) [21], hierarchical DNN [22] and unique preference learning [23] to
evaluate the performance of the EMVO. The proposed EMVO is compared and analyzed with the existing
models in terms of classification accuracy which is shown in Table 8. From the Table 8, the proposed EMVO
model achieves a greater accuracy of 98.65% in YouTube and 98.76% in SAVEE dataset, when compared to
SFLA-IWSS [21] Hierarchical DNN [22] and unique preference learning [23] models, that respectively attain
9. Int J Elec & Comp Eng ISSN: 2088-8708
Facial emotion recognition using enhanced multi-verse optimizer method (Ravi Gummula)
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97.05%, 81.02% and 85.06% in the SAVEE dataset. This clearly states that, the proposed EMVO implements
the classification with greater accuracy and outperforms the existing SFLA-IWSS [21] hierarchical DNN
[22] and unique preference learning [23] models.
Table 8. Comparison evaluation of the proposed model with existing models
Models Classification Accuracy (%) Dataset
SFLA-IWSS [21] 97.81 YouTube
97.05 SAVEE
Hierarchical DNN [22] 81.02 SAVEE
Unique preference learning [23] 85.06 SAVEE
EMVO 98.65 YouTube
98.76 SAVEE
4.5. Discussion
According to the results, the existing methods of shuffled frog leaping algorithm (SFLA) [21],
hierarchical deep neural network (DNN) [22] and unique preference learning [23], are compared with the
proposed method in terms of accuracy. The developed EMVO in this research study aims to further enhance
the accuracy and effectiveness of recognizing emotions in real-time, contributing to advancements in the
field of facial emotion recognition technology. It excels in handling complex and non-linear problems,
making it suitable for the intricate task of recognizing emotions from facial expressions. EMVO's
adaptability allows for efficient learning from limited labeled data, mitigating the need for extensive training
datasets. Facial emotion recognition is a challenging task due to varied possibility of expressions, assessed
under varying lighting conditions and orientations. Thus, the existing methods struggled in optimal feature
selection, and effective navigation of high-dimensional spaces. Additionally, achieving perfect accuracy
often requires a combination of sophisticated feature extraction, model design, and robust data handling. The
limitations of the existing methods, coupled with dataset diversity and challenges in emotion recognition,
prevents achieving better accuracy results, in the datasets of YouTube and SAVEE. In order to mitigate these
existing issues, EMVO was utilized as a potential solution to scale up the exploration and exploitation
efficiency, during feature selection and model training. EMVO, inspired by a more comprehensive search of
the solution space, potentially helps in the discovery of more optimal features for emotion recognition. Its
ability to balance exploration and exploitation better, produces a model design with improved feature
selection which addresses the limitations of existing methods. According to the results, the existing SFLA
[21] achieved accuracy of 97.81% in YouTube and 97.05% in SAVEE dataset. Then, hierarchical DNN [22]
achieved an accuracy of 81.02% in SAVEE dataset. Finally, unique preference learning [23] achieved
accuracy of 85.06% in SAVEE dataset respectively. When compared to existing methods, the suggested
method produced better accuracy performances, that were measured at 98.65% in YouTube and 98.76% in
SAVEE dataset.
5. CONCLUSION
The research of video, audio and text that express user emotions, is an intriguing study that needs
an effective emotion recognition model. Current approaches use various feature selection strategies to
increase emotion identification efficiency. Here, an EMVO technique was suggested to improve the
effectiveness of emotion recognition. In this research, two datasets were used to collect the data, namely
YouTube and SAVEE datasets. Also, the classification was done using CNN to improve emotion
recognition performance. When compared to the existing methods namely SFLA-IWSS, Hierarchical
DNN, and Unique preference learning, the suggested method performed better in terms of accuracy, as it
accomplished 98.65% in YouTube and 98.76% in SAVEE dataset. These results demonstrate superior
performance in emotion identification when compared to existing methods. In the future, the focus will be
on training various classifiers and temporal analysis, for much more accurate emotion recognition in
audio, text, and video data.
AUTHOR CONTRIBUTIONS
The paper conceptualization, methodology, software, validation, formal analysis, investigation,
resources, data curation, writing—original draft preparation, writing—review and editing, visualization, have
been done by 1st
. The supervision and project administration have been done by 2nd
, and 3rd
author.
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BIOGRAPHIES OF AUTHORS
Ravi Gummula is a PhD scholar of electronics and communication engineering at
the Dr. M.G.R. Educational and Research Institute in Chennai, Tamil Nadu, India. He obtained
his M. Tech in VLSI design in 2012 from Shadan College of Engineering and Technology, and
his BE in Electronics and Communication Engineering from Deccan College of Engineering
and Technology in 2007. He can be contacted at email: ravi.gummula@gmail.com.
Vinothkumar Arumugam is a professor of electronics and communication
engineering at the Dr. M.G.R. Educational and Research Institute in Chennai, Tamil Nadu,
India. He obtained his Ph.D. in machine learning in 2017 and his M.Tech. in applied
electronics in 2010 from Dr. M.G.R. Educational and Research Institute, and his BE in
electronics and communication engineering from Anna University in 2008. He received an
M.Sc. in real estate valuation from Annamalai University in 2016. He can be contacted at
email: dravinoth@gmail.com.
Abilasha Aranganathan is an assistant professor of electronics and
communication engineering at the Dr. M.G.R. Educational and Research Institute in Chennai,
Tamil Nadu, India. She obtained her M.Tech. in nanotechnology in 2014 and her BE in
electronics and communication engineering in 2012 from Anna University. She can be
contacted at email: vabilasha90@gmail.com.