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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1181
REAL-TIME DRIVER DROWSINESS DETECTION
Dhivya.M1 M.E, Harish V2, Inbasakaran S3, Aswin Charles L4
1 Assistance Professor, Dept. Of Computer Science and Engineering, Jeppiaar Engineering College, Tamil Nadu,
India
2-4 Student, Dept. Of Computer Science and Engineering, Jeppiaar Engineering College, Tamil Nadu, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Deep learning techniques have been used inorder
to predict the condition and emotion of a driver to provide
information that will improve safety on the road. It is an
application of artificialintelligence. An intelligenceSystem has
been developed to detect the drowsiness of the driver which
can prevent accident and reduces loss and sufferings. A
driver’s condition can be estimated by bio-indicators,
behaviour while driving as well as the expressions on the face
of a driver. In this paper we present an all-inclusive survey of
recent works related to driver drowsiness detection and alert
system. We also present the various deep learning techniques
such as CNN which specially designedto work withimages and
videos, HAAR based cascade classifier, OpenCV which are used
in order to determine the driver’s condition. Finally, we
identify the challenges faced by the current system and it can
be enhanced in future to the vehicles
Key Words: Artificial Intelligence, CNN (Convolutional
neural network), Drowsiness Detection, Deep Learning.
1. INTRODUCTION
One of the foremost frequent causes of road accidentsissaid
to driver’s drowsiness. The statistics show that drowsiness
expose driver to higher crash risks, severe physical injuries,
or maybe death, while the economic losses aren't negligible.
A drowsy driver is in an exceedingly state of mental and
physical flabbiness, which has decreased mental alertness
and a sensation of tiredness. Being during this state, he not
performs competence to safety driving man oeuvre. Drowsy
driving could be a real problem in our society because it
affects and puts at risk all traffic participants - drivers and
pedestrians. the event of a system, which monitors, in real
time, the driver’s level of drowsiness will decrease the
amount of car accidents and can save immeasurable lives
everywhere the globe. the employment of such an assisting
system, ready to measure the amount of vigilance is critical
in car crash prevention. so as to developthesystemisvital to
understand to judge the extent of drowsiness. Four kinds of
measurements are commonly accustomed check the extent
of drowsiness. There are several approaches employed in
face detection. a number of these encode the knowledge
about characteristics of typical face and find structural
elements - like eyebrows, eyes, nose, mouth and hairline-
and use the relationships between them to detect faces. in a
very method to spot the face from a cluttered background
supported segmentation was proposed. The eclipse was
fitted to the boundary between the top region and also the
background and also the face are going to be detected. The
human coloring and texture faces have also proven to be
good features for face detection. For this method, the
foremost important feature was the color which will be
separated from other parts of the background. This method
used maximal varieties variance threshold. Anothermethod
used for face detection was the histogram intersection
within the HSV color space to spotlight the skin region. The
template matching methodsstoreseveral patternsofvarious
faces to explain as a full or the face expression separately,by
computing the correlations between aninputimageandalso
the stored pattern so as to work out the degree of similarity
of the pattern to a face. There are several techniques which
are used with this method. For detecting the features from
the face here we use the CNN algorithm which extracts the
features from the image screens. By detectingthefeaturesof
eyes from the image, whether it's closed or opened we are
able to identify the Drowsiness level of the motive force.
2. LITERATURE REVIEW
The developed system may be a real time system. HAAR
based cascade classifier is employed for face detection. An
algorithm to trace objects are wont to track the eyes
continuously. so as to spot the drowsy state of the driving
force, the CNN algorithm is employed. The paper focuses on
developing a non-interfering system which might detect
fatigue and issue a warning on time.Thesystemwill monitor
the driver’s face employing a camera. By developing an
algorithm, the drowsy symptomsofdriveraredetected early
enough to avoid accident. When the signs of fatigue are
identified then a sound are produced as an attentive to the
driving force unless he's awake from fatigue.Alertsoundare
deactivated automatically when the driving force is awake
from the fatigue for a period of your time. this method will
detect driver’s fatigue by the processing of the attention
region. After image acquisition, the primary stage of
processing is face detection. If the eyes are closed for quite
0.7 seconds, this technique issues alert sound to the motive
force. System makes use of the amount of eye blinks for
detecting the state of drowsiness in an exceedingly driver. If
eye of the driver is blinking it will not consider that as an
issue. The system makes use of OpenCV and a camera. the
attention status is obtained through image processing
algorithms. this method takes under consideration only the
state of the eyes, it doesn't concentrate on the frequency of
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1182
yawning. during this system computer vision are wont to
detect drowsiness. Eye closureisdetectedusingHAARbased
cascade classifier this method includes two modules. the 2
modules are the face and eye detection module followed by
the face tracking module. during this system convolutional
layer is employed, a layer with series of mathematical
operations is performed to extract the feature map of the
input image. this technique alsousescascadeclassifierssoas
to boost the accuracy of face detection. The status of the
attention and therefore the time taken is noted as score. If
the status is open then the score is zero.Ifdriverfeelsfatigue
and also the status changes to shut supported the videothen
the score will increase and an alarm sound are going to be
raised to awake the motive force from fatigue.
3. OBJECTIVE
The main objective of the system is accurately detecting a
driver’s drowsiness supported eyelid movementanditgives
an appropriate voice alert in real-time. the opposite
objectives include designing a system that detects
drowsiness of drivers by monitoring the eyes of the motive
force regularly, especially the retina. The systemshouldgive
an attentive to the driving force when the driver’s eyes
remain closed for some seconds.Thesystemperformsbetter
even a driver is wearing spectacles. The system will give an
alert sound for a particular period of your time until the
fatigue drivers eyes is open normally with none drowsiness.
this technique doesn't affect by bad lighting
4. METHODOLOGY
Firstly, the face is localized in the image using facial
landmark detection. Then, shape prediction methods are
used to detect important features on the face. Facedetection
is done by HAAR cascades, which are pre-trained.Inthe next
step, to estimate the location of (x, y)-coordinates that map
to facial structures. My model file was trained onCNN,sothe
best weights are already obtained for the model. The CNN
consists of fully connected layer of 128 nodes. OpenCV is
been used for gathering the images from camera and feed
them into a Deep Learningmodel whichwill classifywhether
the person’s eyes are ‘Open’ or ‘Closed’ respectively.Thenan
alarm sound is used as alert sound for the drivers to awake
from the fatigue.
5. SYSTEM DESIGN
5.1 System Architecture
When the driver is driving is face captured by the camera
and then it is converted into frames. Then the system
analyses the drowsiness and amount of fatigue level. The
detected facial image is processed to check whethertheeyes
of the driver is ‘Open’ or ‘Closed’. If the eyes are opened then
the alarm will be set off. if the eyes are closed for a certain
period of time which is calculated as score. If the score is
above the limitation, then an alarm sound will be raised to
give an alert to the fatigue driver until the driver’s eye is
opened for a certain period of time. The alert sound will
terminate automatically when the driver is away from the
fatigue for a period of time. The flow of the system activity is
clearly described in the Fig.1
Fig -1: System Activity
Fig -2: Data Flow in the System
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1183
Fig -3: Use case Diagram
5.2 Detailed design
The eyes and mouth of the driver are always monitored by
the camera which will be attached in the dashboard of the
vehicle and if the predefined levels of alertnessareobserved
to be defaulted and compromised,thenanappropriatealarm
is set off, and accordingly, action is taken to prevent any
fatalities. Fig.4 depicts the System Design of Driver
Drowsiness. It can be seen that the camera is used for
monitoring the driver’s face continuously and upon
detection of drowsiness or fatigue, the system in the
dashboard generates a alert sound continuously until the
driver awakes from the drowsiness.
Fig -4: System Design
6. EXPERIMENTAL RESULTS
6.1 Experimental Dataset
The experimental dataset used in our project is a CNN
trained model, so the best weights are already obtained for
the model. We wrote a script to collect the images from
camera then we separated them into their respective labels
‘Open’ or ‘Closed’. The data was checked and the unwanted
images has been removed which is mot necessary to build
the model. the data comprises around 7000 images of
people’s eye under different lighting conditions. The dataset
covers a large variety of identities, face size, lighting
conditions, pose, etc. The dataset covers a large variety of
identities, face size, lighting conditions, pose, etc. After
training our model it is attached as model architecture file
“models/cnnCat2.h5.
6.2 Performance Analysis
A large amount of picture was taken to achieve these
expected results. Fig.5 represents the output when the eyes
is opened. the score will also be remained zero and no alarm
sound will be raised. Fig.6 represents the output when the
eyes is closed and the driver feels fatigue for a certainperiod
of time. the score will be raised accordinglytothetimetaken
if the score crosses its limit, then the alarm sound will be
produced to create an alert to the driver, so that driver can
awake from the fatigue state.
Fig -5: Eyes opened and the driver is in normal state
Fig -6: Eyes closed and the driver is in fatigue state
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1184
7. CHALLENGES
The accuracy of the model degrades if the attention frames
aren't captured clearly thanks to any quite obstacles like
goggles, sunglasses, spectacles (having reflection). Camera
operations like auto adjustments with regard to zoom and
rotation aren't considered in conducting experiments. Once
the eyes are limited in position, zooming the camera will
help to increase the accuracy. The accuracy of detection of
eyes and mouth reduces when the motive force isn't facing
the camera
8. FUTURE ENHANCEMENT
The model is improved incrementally by using other
parameters like blink rate, yawning, state of the car, etc. If of
these parameters are used it can improve the accuracy by
lots. We conceive to further work on the project by adding a
sensor to trace the guts rate so as to forestall accidents
caused thanks to sudden heart attacks to drivers. Same
model and techniques may be used for various other uses
like Netflix and other streamingservicescandetectwhenthe
user is asleep and stop the video accordingly. It can even be
utilized in application that stops user from sleeping.
3. CONCLUSIONS
This project presentsa prototypeimplementedinsmall scale
actually. It shows implementation under various scenarios
like low light, dark and also the system seems to provide
efficient results under these conditions. In future we decide
to improve the programme which wasn't of much
importance currently. Moreover, we attempt to extend the
research by analyzing road conditions too with the
assistance of rear camera. With some more improvements
the system is placed in use for general public.
REFERENCES
[1] J. Jang, S. Jeon, J. Kim, H. and Yoon, “Robust Deep Age
Estimation Method Using Artificially Generated Image
Set,” ETRI Journal, vol. 39, no. 5, pp. 643-651, Oct., 2017.
[2] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu,
and A. Berg, “SSD: Single Shot MultiBox Detector,” Proc.
of the European Conference on Computer Vision 2016,
Oct. 2016.
[3] ] J. Redmon, S. Divvals, R. Girshick, and A. Parhadi, “You
Only Look Once: Unified, Real-Time Object Detection,”
Proc. of International Conference on Computer Vision
and Pattern Recognition 2016, pp. 779-788, Jun. 2016.
[4] T. Danisman, I. Bilasco, C. Djeraba and N. Ihaddadene,
“Drowsy driver detection system using eye blink
patterns,” Proc. Of International ConferenceonMachine
and Web Intelligence, 2016.
[5] T. Soukupova and J. Cech, “Real-time eye blink detection
using facial landmarks,” Proc. of Computer Vision
Winter Workshop,2016. [11] K. Ban, J. Kim, andH.Yoon,
“Gender Classification of Low-Resolution Facial Image
Based on Pixel Classifier Boosting,” ETRI Journal vol.38,
no. 2, pp. 347-355. Apr. 2016.

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REAL-TIME DRIVER DROWSINESS DETECTION

  • 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1181 REAL-TIME DRIVER DROWSINESS DETECTION Dhivya.M1 M.E, Harish V2, Inbasakaran S3, Aswin Charles L4 1 Assistance Professor, Dept. Of Computer Science and Engineering, Jeppiaar Engineering College, Tamil Nadu, India 2-4 Student, Dept. Of Computer Science and Engineering, Jeppiaar Engineering College, Tamil Nadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Deep learning techniques have been used inorder to predict the condition and emotion of a driver to provide information that will improve safety on the road. It is an application of artificialintelligence. An intelligenceSystem has been developed to detect the drowsiness of the driver which can prevent accident and reduces loss and sufferings. A driver’s condition can be estimated by bio-indicators, behaviour while driving as well as the expressions on the face of a driver. In this paper we present an all-inclusive survey of recent works related to driver drowsiness detection and alert system. We also present the various deep learning techniques such as CNN which specially designedto work withimages and videos, HAAR based cascade classifier, OpenCV which are used in order to determine the driver’s condition. Finally, we identify the challenges faced by the current system and it can be enhanced in future to the vehicles Key Words: Artificial Intelligence, CNN (Convolutional neural network), Drowsiness Detection, Deep Learning. 1. INTRODUCTION One of the foremost frequent causes of road accidentsissaid to driver’s drowsiness. The statistics show that drowsiness expose driver to higher crash risks, severe physical injuries, or maybe death, while the economic losses aren't negligible. A drowsy driver is in an exceedingly state of mental and physical flabbiness, which has decreased mental alertness and a sensation of tiredness. Being during this state, he not performs competence to safety driving man oeuvre. Drowsy driving could be a real problem in our society because it affects and puts at risk all traffic participants - drivers and pedestrians. the event of a system, which monitors, in real time, the driver’s level of drowsiness will decrease the amount of car accidents and can save immeasurable lives everywhere the globe. the employment of such an assisting system, ready to measure the amount of vigilance is critical in car crash prevention. so as to developthesystemisvital to understand to judge the extent of drowsiness. Four kinds of measurements are commonly accustomed check the extent of drowsiness. There are several approaches employed in face detection. a number of these encode the knowledge about characteristics of typical face and find structural elements - like eyebrows, eyes, nose, mouth and hairline- and use the relationships between them to detect faces. in a very method to spot the face from a cluttered background supported segmentation was proposed. The eclipse was fitted to the boundary between the top region and also the background and also the face are going to be detected. The human coloring and texture faces have also proven to be good features for face detection. For this method, the foremost important feature was the color which will be separated from other parts of the background. This method used maximal varieties variance threshold. Anothermethod used for face detection was the histogram intersection within the HSV color space to spotlight the skin region. The template matching methodsstoreseveral patternsofvarious faces to explain as a full or the face expression separately,by computing the correlations between aninputimageandalso the stored pattern so as to work out the degree of similarity of the pattern to a face. There are several techniques which are used with this method. For detecting the features from the face here we use the CNN algorithm which extracts the features from the image screens. By detectingthefeaturesof eyes from the image, whether it's closed or opened we are able to identify the Drowsiness level of the motive force. 2. LITERATURE REVIEW The developed system may be a real time system. HAAR based cascade classifier is employed for face detection. An algorithm to trace objects are wont to track the eyes continuously. so as to spot the drowsy state of the driving force, the CNN algorithm is employed. The paper focuses on developing a non-interfering system which might detect fatigue and issue a warning on time.Thesystemwill monitor the driver’s face employing a camera. By developing an algorithm, the drowsy symptomsofdriveraredetected early enough to avoid accident. When the signs of fatigue are identified then a sound are produced as an attentive to the driving force unless he's awake from fatigue.Alertsoundare deactivated automatically when the driving force is awake from the fatigue for a period of your time. this method will detect driver’s fatigue by the processing of the attention region. After image acquisition, the primary stage of processing is face detection. If the eyes are closed for quite 0.7 seconds, this technique issues alert sound to the motive force. System makes use of the amount of eye blinks for detecting the state of drowsiness in an exceedingly driver. If eye of the driver is blinking it will not consider that as an issue. The system makes use of OpenCV and a camera. the attention status is obtained through image processing algorithms. this method takes under consideration only the state of the eyes, it doesn't concentrate on the frequency of
  • 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1182 yawning. during this system computer vision are wont to detect drowsiness. Eye closureisdetectedusingHAARbased cascade classifier this method includes two modules. the 2 modules are the face and eye detection module followed by the face tracking module. during this system convolutional layer is employed, a layer with series of mathematical operations is performed to extract the feature map of the input image. this technique alsousescascadeclassifierssoas to boost the accuracy of face detection. The status of the attention and therefore the time taken is noted as score. If the status is open then the score is zero.Ifdriverfeelsfatigue and also the status changes to shut supported the videothen the score will increase and an alarm sound are going to be raised to awake the motive force from fatigue. 3. OBJECTIVE The main objective of the system is accurately detecting a driver’s drowsiness supported eyelid movementanditgives an appropriate voice alert in real-time. the opposite objectives include designing a system that detects drowsiness of drivers by monitoring the eyes of the motive force regularly, especially the retina. The systemshouldgive an attentive to the driving force when the driver’s eyes remain closed for some seconds.Thesystemperformsbetter even a driver is wearing spectacles. The system will give an alert sound for a particular period of your time until the fatigue drivers eyes is open normally with none drowsiness. this technique doesn't affect by bad lighting 4. METHODOLOGY Firstly, the face is localized in the image using facial landmark detection. Then, shape prediction methods are used to detect important features on the face. Facedetection is done by HAAR cascades, which are pre-trained.Inthe next step, to estimate the location of (x, y)-coordinates that map to facial structures. My model file was trained onCNN,sothe best weights are already obtained for the model. The CNN consists of fully connected layer of 128 nodes. OpenCV is been used for gathering the images from camera and feed them into a Deep Learningmodel whichwill classifywhether the person’s eyes are ‘Open’ or ‘Closed’ respectively.Thenan alarm sound is used as alert sound for the drivers to awake from the fatigue. 5. SYSTEM DESIGN 5.1 System Architecture When the driver is driving is face captured by the camera and then it is converted into frames. Then the system analyses the drowsiness and amount of fatigue level. The detected facial image is processed to check whethertheeyes of the driver is ‘Open’ or ‘Closed’. If the eyes are opened then the alarm will be set off. if the eyes are closed for a certain period of time which is calculated as score. If the score is above the limitation, then an alarm sound will be raised to give an alert to the fatigue driver until the driver’s eye is opened for a certain period of time. The alert sound will terminate automatically when the driver is away from the fatigue for a period of time. The flow of the system activity is clearly described in the Fig.1 Fig -1: System Activity Fig -2: Data Flow in the System
  • 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1183 Fig -3: Use case Diagram 5.2 Detailed design The eyes and mouth of the driver are always monitored by the camera which will be attached in the dashboard of the vehicle and if the predefined levels of alertnessareobserved to be defaulted and compromised,thenanappropriatealarm is set off, and accordingly, action is taken to prevent any fatalities. Fig.4 depicts the System Design of Driver Drowsiness. It can be seen that the camera is used for monitoring the driver’s face continuously and upon detection of drowsiness or fatigue, the system in the dashboard generates a alert sound continuously until the driver awakes from the drowsiness. Fig -4: System Design 6. EXPERIMENTAL RESULTS 6.1 Experimental Dataset The experimental dataset used in our project is a CNN trained model, so the best weights are already obtained for the model. We wrote a script to collect the images from camera then we separated them into their respective labels ‘Open’ or ‘Closed’. The data was checked and the unwanted images has been removed which is mot necessary to build the model. the data comprises around 7000 images of people’s eye under different lighting conditions. The dataset covers a large variety of identities, face size, lighting conditions, pose, etc. The dataset covers a large variety of identities, face size, lighting conditions, pose, etc. After training our model it is attached as model architecture file “models/cnnCat2.h5. 6.2 Performance Analysis A large amount of picture was taken to achieve these expected results. Fig.5 represents the output when the eyes is opened. the score will also be remained zero and no alarm sound will be raised. Fig.6 represents the output when the eyes is closed and the driver feels fatigue for a certainperiod of time. the score will be raised accordinglytothetimetaken if the score crosses its limit, then the alarm sound will be produced to create an alert to the driver, so that driver can awake from the fatigue state. Fig -5: Eyes opened and the driver is in normal state Fig -6: Eyes closed and the driver is in fatigue state
  • 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1184 7. CHALLENGES The accuracy of the model degrades if the attention frames aren't captured clearly thanks to any quite obstacles like goggles, sunglasses, spectacles (having reflection). Camera operations like auto adjustments with regard to zoom and rotation aren't considered in conducting experiments. Once the eyes are limited in position, zooming the camera will help to increase the accuracy. The accuracy of detection of eyes and mouth reduces when the motive force isn't facing the camera 8. FUTURE ENHANCEMENT The model is improved incrementally by using other parameters like blink rate, yawning, state of the car, etc. If of these parameters are used it can improve the accuracy by lots. We conceive to further work on the project by adding a sensor to trace the guts rate so as to forestall accidents caused thanks to sudden heart attacks to drivers. Same model and techniques may be used for various other uses like Netflix and other streamingservicescandetectwhenthe user is asleep and stop the video accordingly. It can even be utilized in application that stops user from sleeping. 3. CONCLUSIONS This project presentsa prototypeimplementedinsmall scale actually. It shows implementation under various scenarios like low light, dark and also the system seems to provide efficient results under these conditions. In future we decide to improve the programme which wasn't of much importance currently. Moreover, we attempt to extend the research by analyzing road conditions too with the assistance of rear camera. With some more improvements the system is placed in use for general public. REFERENCES [1] J. Jang, S. Jeon, J. Kim, H. and Yoon, “Robust Deep Age Estimation Method Using Artificially Generated Image Set,” ETRI Journal, vol. 39, no. 5, pp. 643-651, Oct., 2017. [2] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, and A. Berg, “SSD: Single Shot MultiBox Detector,” Proc. of the European Conference on Computer Vision 2016, Oct. 2016. [3] ] J. Redmon, S. Divvals, R. Girshick, and A. Parhadi, “You Only Look Once: Unified, Real-Time Object Detection,” Proc. of International Conference on Computer Vision and Pattern Recognition 2016, pp. 779-788, Jun. 2016. [4] T. Danisman, I. Bilasco, C. Djeraba and N. Ihaddadene, “Drowsy driver detection system using eye blink patterns,” Proc. Of International ConferenceonMachine and Web Intelligence, 2016. [5] T. Soukupova and J. Cech, “Real-time eye blink detection using facial landmarks,” Proc. of Computer Vision Winter Workshop,2016. [11] K. Ban, J. Kim, andH.Yoon, “Gender Classification of Low-Resolution Facial Image Based on Pixel Classifier Boosting,” ETRI Journal vol.38, no. 2, pp. 347-355. Apr. 2016.