Synopsis of Face Recognition System.

1. FACE RECOGNITION SYSTEM
ABSTRACT
Face (facial) recognition is the identification of humans by the unique characteristics of
their Faces. Face recognition technology is the least intrusive and fastest bio-metric
technology. It works with the most obvious individual identifier of the human face. With
increasing security needs and with advancement in technology extracting information has
become much simpler. This system based on face recognition using different algorithms
and comparing the results. The basic purpose being to identify the face and retrieving
information stored in database. It involves two main steps. First to identify the
distinguishing factors in image and storing them and Second step to compare it with the
existing images and returning the data related to that image. The various algorithms used
for face detection are PCAAlgorithm and Gray Scale Algorithm etc.

2. FACE RECOGNITION SYSTEM
INTRODUCTION:
1) BIO-METRICS:
Biometrics is used in the process of authentication of a person by verifying or identifying
that a user requesting a network resource is who he, she, or it claims to be, and vice versa.
It uses the property that a human trait associated with a person itself like structure of data
with the incoming data we can verify the identity of a particular person. There are many
types of biometric system like detection and recognition, iris recognition etc., these traits
are used for human identification in surveillance system, criminal identification , face
details etc. By comparing the existing fingerprint recognition.
2) FACE RECOGNITION:
Human beings have recognition capabilities that are unparalleled in the modern
computing era. These are mainly due to the high degree of interconnectivity, adaptive
nature, learning skills and generalization capabilities of the nervous system. The human
brain has numerous highly interconnected biological neurons which, on some specific
tasks, can outperform super computers. A child can accurately identify a face, but for a
computer it is a cumbersome task. Therefore, the main idea is to engineer a system which
can emulate what a child can do. Advancements in computing capability over the past
few decades have enabled comparable recognition capabilities from such engineered
systems quite successfully. Early face recognition algorithms used simple geometric
models, but recently the recognition process has now matured into a science of
sophisticated mathematical representations and matching processes. Major advancements
and initiatives have propelled face recognition technology into the spotlight. Face
recognition technology can be used in wide range of applications. Computers that detect
and recognize faces could be applied to a wide variety of practical applications including
criminal identification etc. Face detection and recognition is used in many places now a
days , verifying websites hosting images and social networking sites. Face recognition
and detection can be achieved using technologies related to computer science.
Face Recognition can be of two types:

3. FACE RECOGNITION SYSTEM
1) Feature Based (Geometric)
2) Template Based (Photometric)
In geometric or feature-based methods, facial features such as eyes, nose, mouth, and
chin are detected. Properties and relations such as areas, distances, and angles between
the features are used as descriptors of faces. Although this class of methods is economical
and efficient in achieving data reduction and is insensitive to variations in illumination
and viewpoint, it relies heavily on the extraction and measurement of facial features. In
contrast, template matching and neural methods generally operate directly on an image-
based representation of faces, i.e., pixel intensity array. Because the detection and
measurement of geometric facial features are not required, this type of method has been
more practical and easier to implement when compared to geometric feature-based
methods.
LITERATURE SURVEY:
Template Matching Approach for Face Recognition System:
( Sadhna Sharma “Template Matching Approach for Face Recognition System”, National
Taiwan Ocean University, Taiwan, 2013.)
Face recognition is a specific and hard case of object recognition. The difficulty of this
problem stems from the fact that in their most common form (i.e., the frontal view) faces
appear to be roughly alike and the differences between them are quite subtle.
Consequently, frontal face images form a very dense cluster in image space which makes
it virtually impossible for traditional pattern recognition techniques to accurately
discriminate among them with a high degree of success . Though it is much easier to
install face recognition system in a large setting, the actual implementation is very
challenging as it needs to account for all possible appearance variation caused by change
in illumination, facial features, variations in pose, image resolution, sensor noise, viewing
distance, occlusions, etc.
Comparative Analysis of advanced Face Recognition Techniques :

4. FACE RECOGNITION SYSTEM
(MS.P.JENNIFER , DR. A. MUTHU KUMARAVEL “Comparative Analysis of advanced
Face Recognition Techniques” International Journal of Innovative Research in Computer
and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2,
Issue 7, July 2014 . )
This is based on fuzzy c means clustering and associated sub neural network. It deals
with the face is a complex multidimensional visual model and developing a
computational model for face recognition is difficult. In this paper, author represents a
method for face recognition based on similar neural networks. Neural networks (NNs)
have been widely used in various fields. However, the computing effectiveness
decreases rapidly if the scale of the NN increases. In this paper, a new method of face
recognition based on fuzzy clustering and parallel NNs is proposed. The face patterns are
divided into several small-scale neural networks based on fuzzy clustering and they are
combine to obtain the recognition result.
Video-to-Video Face Matching: Establishing a Baseline for Unconstrained Face
Recognition:
(Lacey Best-Rowden, Brendan Klare , Joshua Klontz , Anil K. Jain” Video-to-Video Face
Matching: Establishing a Baseline for Unconstrained Face Recognition ” ,Michigan State
University, East Lansing, MI, U.S.A. Noblis, Falls Church, VA, U.S.A. ,2013.)
Face recognition in video is becoming increasingly important due to the abundance of
video data captured by surveillance cameras, mobile devices, Internet uploads, and other
sources. Given the aggregate of facial information contained in a video (i.e., a sequence
of face images or frames), video-based face recognition solutions can potentially alleviate
classic challenges caused by variations in pose, illumination, and expression. However,
with this increased focus on the development of algorithms specifically crafted for video-
based face recognition, it is important to establish a baseline for the accuracy using state-
of-threat still image matchers. Note that most commercial-off the-shelf (COTS) offerings
are still limited to single frame matching.
Image Analysis for Face Recognition:

5. FACE RECOGNITION SYSTEM
(Xiaoguang Lu “Image Analysis for Face Recognition ”Dept. of Computer Science &
Engineering Michigan State University, East Lansing, MI, 48824,2014.)
Three classical linear appearance-based classifiers, PCA, ICA and LDA are introduced in
the following. Each classifier has it’s own representation (basis vectors) of a high
dimensional face vector space based on different statistical viewpoints. By projecting the
face vector to the basis vectors, the projection coefficients are used as the feature
representation of each face image. The matching score between the test face image and
the training prototype is calculated (e.g., as the cosine value of the angle) between their
coefficients vectors. The larger the matching score, the better the match.
Mutual Component Analysis for Heterogeneous Face Recognition:
(ZHIFENG LI and DIHONG GONG, Chinese Academy of Sciences , QIANG LI and
DACHENG TAO, University of Technology Sydney ,XUELONG LI, Chinese Academy
of Sciences “Mutual Component Analysis for Heterogeneous Face Recognition” 2016.)
Heterogeneous face recognition, also known as cross-modality face recognition or Inter
modality face recognition, refers to matching two face images from alternative image
modalities. Author proposed a novel approach called Mutual Component Analysis
(MCA) to infer the mutual components for robust heterogeneous face recognition. In the
MCA approach, a generative model is first proposed to model the process of generating
face images in different modalities, and then an Expectation Maximization (EM)
algorithm is designed to iteratively learn the model parameters.
Detection of Subconscious Face Recognition Using Consumer-Grade Brain-
Computer Interfaces :
(MIGUEL VARGAS MARTIN, VICTOR CHO, and GABRIEL AVERSANO, “Detection
of Subconscious Face Recognition Using Consumer-Grade Brain-Computer Interfaces”,
University of Ontario Institute of Technology,2016.)
Author test the possibility of tapping the subconscious mind for face recognition using
consumer-grade BCIs. To this end, Author performed an experiment whereby subjects
were presented with photographs of famous persons with the expectation that about 20%

6. FACE RECOGNITION SYSTEM
of them would be (consciously) recognized; and since the photos are of famous persons,
Author expected that subjects would have seen before some of the 80% they didn’t
(consciously) recognize. Author analyzed a number of event related potentials training
and testing a support vector machine. Author found that our method is capable of
differentiating between no recognitions and subconscious recognitions with promising
accuracy levels, suggesting that tapping the subconscious mind for face recognition is
feasible.
Face recognition using the most representative SIFT image :
(SSAM DAGHER,NOUR EI SALLAK & HANI HAZIM,” Face recognition using the
most representative SIFT image” 2014.)
In this paper Face recognition using the most representative SIFT images is presented. It
is based on obtaining the SIFT (SCALE INVARIANT FEATURE TRANSFORM)
features in different regions of each training image . Those regions were obtained using
K-means clustering algorithm applied on the key points obtained from the SIFT
algorithm .Based on this features an algorithm which will get the most representative
images of each face is presented.
A Comprehensive Survey on Pose-Invariant Face Recognition :
(CHANGXING DING and DACHENG TAO, “A Comprehensive Survey on Pose-
Invariant Face Recognition”, University of Technology Sydney,2016.)
PIFR (Pose-Invariant Recognition) is the primary stumbling block to realizing the full
potential of face recognition as a passive biometric technology. This fundamental human
ability poses a great challenge for computer vision systems. The difficulty stems from the
immense within-class appearance variations caused by pose change, for example, self-
occlusion, nonlinear texture distortion, and coupled illumination or expression variations.
In this article, Author reviewed representative PIFR algorithms and classified them into
four broad categories according to their strategy to bridge the cross-pose gap: pose robust
feature extraction, multi view subspace learning, face synthesis, and hybrid approaches
Smart Cane: Face Recognition System for Blind :

7. FACE RECOGNITION SYSTEM
(Yongsik Jin, Jonghong Kim, Bumhwi Kim, Rammohan Mallipeddi, Minho Lee* ,”
Smart Cane: Face Recognition System for Blind”,Kyungpook National University
Daegu, Korea ,2015.)
Author proposed a smart cane with a face recognition system to help the blind in
recognizing human faces. This system detects and recognizes faces around them. The
result of the detection is informed to the blind person through a vibration pattern. The
proposed system was designed to be used in real-time and is equipped with a camera
mounted on the glasses, a vibration motor attached to the cane and a mobile computer.
The camera attached to the glasses sends image to mobile computer. The mobile
computer extracts features from the image and then detects the face using Ad boost. We
use the modified census transform (MCT) descriptor for feature extraction. After face
detection, the information regarding the detected face image is gathered. We used
compressed sensing with L2-norm as a classifier. Cane is equipped with a Bluetooth
module and receives a person’s information from the mobile computer. The cane
generates vibration patterns unique to each person as to inform a blind person about the
identity of the detected person using the camera. Hence, the blind people can know the
person standing in front of them.
AN EFFICIENT HYBRID REAL TIME FACE RECOGNITION ALGORITHM IN
JAVA ENVIRONMENT:
(M. A. Abdou*, M. H. Fathy** “AN EFFICIENT HYBRID REAL TIME FACE
RECOGNITION ALGORITHM IN JAVA ENVIRONMENT”, Electrical Engineering
Department, Pharos University in Alexandria, Alexandria, EGYPT ,2015.)
Image processing on mobile smart phones is a new and exciting field with many
challenges due to limited hardware and connectivity problems. Android based mobile
phones are now becoming the core of many applications. This paper develops a real time
face recognition application model for smart phones. This introduced model uses a hybrid
skin color-Eigen face detection method and an interest point localization for feature
matching. The paper is coded in JAVA programming language to fulfill Android smart
phones. Results are shown and compared with existing open source techniques for

8. FACE RECOGNITION SYSTEM
verification. The aim is to maintain real time measures with high recognition rate.
Applications range from security to people with disabilities adaptation.
OBJECTIVE:
1) Trying to find a face within a large database of faces. In this approach the
system returns a possible list of faces from the database. The most useful
applications contain crowd surveillance, video content indexing, personal
identification (example: drivers license), mug shots matching, etc.
2) Real time face recognition: Here, face recognition is used to identify a person
on the spot and grant access to a building or a compound, thus avoiding
security hassles. In this case the face is compared against a multiple training
samples of a person.
PROBLEM STATEMENT:
Face Recognition human facial features like the mouth, nose and eyes in a full
frontal face image. We will be adapting a multi-step process in order to achieve the goal.
To detect the face region we will be using a skin-color segmentation method.
Morphological techniques will be adapted to fill the holes that would be created after the
segmentation process. Facial features can be located in the interior of the face contour.
We will use several different facial-images to test our method.
FUTURE SCOPE:
Face recognition has become a popular area of research in computer vision and
one of the most successful applications of image analysis and understanding. Because of
the nature of the problem, not only computer science researchers are interested in it, but
neuron scientists and psychologists also. Face recognition systems used today work very
well under constrained conditions, although all systems work much better with frontal
mug-shot images and constant lighting. All current face recognition algorithms fail under
the vastly varying conditions under which humans need to and are able to identify other

9. FACE RECOGNITION SYSTEM
people. Next generation person recognition systems will need to recognize people in real-
time and in much less constrained situations.

10. FACE RECOGNITION SYSTEM
REFERENCES:
[1] Sadhna Sharma “Template Matching Approach for Face Recognition System”,
National Taiwan Ocean University, Taiwan,2013.
[2] MS.P.JENNIFER , DR. A. MUTHU KUMARAVEL “Comparative Analysis of
advanced Face Recognition Techniques” International Journal of Innovative
Research in Computer
and Communication Engineering (An ISO 3297: 2007 Certified Organization)
Vol. 2, Issue 7, July 2014 .
[3] Lacey Best-Rowden, Brendan Klare , Joshua Klontz , Anil K. Jain” Video-to-
Video Face Matching: Establishing a Baseline for Unconstrained Face
Recognition ” ,Michigan State University, East Lansing, MI, U.S.A. Noblis, Falls
Church, VA, U.S.A. ,2013.
[4] Xiaoguang Lu “Image Analysis for Face Recognition ”Dept. of Computer Science
& Engineering Michigan State University, East Lansing, MI, 48824,2014.
[5] ZHIFENG LI and DIHONG GONG, Chinese Academy of Sciences , QIANG LI
and DACHENG TAO, University of Technology Sydney ,XUELONG LI,
Chinese Academy of Sciences “Mutual Component Analysis for Heterogeneous
Face Recognition” 2016.
[6] MIGUEL VARGAS MARTIN, VICTOR CHO, and GABRIEL AVERSANO,
“Detection of Subconscious Face Recognition Using Consumer-Grade Brain-
Computer Interfaces”, University of Ontario Institute of Technology,2016.
[7] ISSAM DAGHER,NOUR EI SALLAK & HANI HAZIM,” Face recognition
using the most representative SIFT image” 2014.
[8] CHANGXING DING and DACHENG TAO, “A Comprehensive Survey on Pose-
Invariant Face Recognition”, University of Technology Sydney,2016.
[9] Yongsik Jin, Jonghong Kim, Bumhwi Kim, Rammohan Mallipeddi, Minho Lee*
,” Smart Cane: Face Recognition System for Blind”,Kyungpook National
University Daegu, Korea ,2015.
[10] M. A. Abdou*, M. H. Fathy** “AN EFFICIENT HYBRID REAL TIME FACE
RECOGNITION ALGORITHM IN JAVA ENVIRONMENT”, Electrical

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Engineering Department, Pharos University in Alexandria, Alexandria,
EGYPT,2015.

12. FACE RECOGNITION SYSTEM