International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly
in recent years and it is more direct, user friendly and convenient compared to other methods. But face
recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face
recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness
detection in order to guard against such spoofing. In this work, face liveness detection approaches are
categorized based on the various types techniques used for liveness detection. This categorization helps
understanding different spoof attacks scenarios and their relation to the developed solutions. A review of
the latest works regarding face liveness detection works is presented. The main aim is to provide a simple
path for the future development of novel and more secured face liveness detection approach.
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...ijtsrd
In todays world, there are number of existing methods for facial recognition. These methods are based on frontal view face data. There are few methods which are based on non-frontal view face recognition method. In most of the face recognition algorithm, œFeature space approach is used. In this approach, different feature vectors are extracted from face. These distances are compared to determine matches. In this paper, it is proposed that how any person can be located in a campus or in a city using a cross pose face recognition method. This paper is focusing on three parts 1) generation of multi-view images 2) comparison of images 3) showing the actual location of a person. Sanjay D. Sawaitul"Cross Pose Facial Recognition Method for Tracking any Persons Location an Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7186.pdf http://www.ijtsrd.com/computer-science/data-processing/7186/cross-pose-facial-recognition-method-for--tracking-any-persons-location-an-approach/sanjay-d-sawaitul
A comparative review of various approaches for feature extraction in Face rec...Vishnupriya T H
Four feature extraction algorithms are discussed here.
1. Principal Component Analysis
2. Discreet LInear Transform
3. Independent Component Analysis
4. Linear Discriminant Aalysis
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly
in recent years and it is more direct, user friendly and convenient compared to other methods. But face
recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face
recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness
detection in order to guard against such spoofing. In this work, face liveness detection approaches are
categorized based on the various types techniques used for liveness detection. This categorization helps
understanding different spoof attacks scenarios and their relation to the developed solutions. A review of
the latest works regarding face liveness detection works is presented. The main aim is to provide a simple
path for the future development of novel and more secured face liveness detection approach.
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...ijtsrd
In todays world, there are number of existing methods for facial recognition. These methods are based on frontal view face data. There are few methods which are based on non-frontal view face recognition method. In most of the face recognition algorithm, œFeature space approach is used. In this approach, different feature vectors are extracted from face. These distances are compared to determine matches. In this paper, it is proposed that how any person can be located in a campus or in a city using a cross pose face recognition method. This paper is focusing on three parts 1) generation of multi-view images 2) comparison of images 3) showing the actual location of a person. Sanjay D. Sawaitul"Cross Pose Facial Recognition Method for Tracking any Persons Location an Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7186.pdf http://www.ijtsrd.com/computer-science/data-processing/7186/cross-pose-facial-recognition-method-for--tracking-any-persons-location-an-approach/sanjay-d-sawaitul
A comparative review of various approaches for feature extraction in Face rec...Vishnupriya T H
Four feature extraction algorithms are discussed here.
1. Principal Component Analysis
2. Discreet LInear Transform
3. Independent Component Analysis
4. Linear Discriminant Aalysis
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
CDS is the criminal face identification by capsule neural network.
Solving the common problems in image recognition such as illumination problem, scale variability, and to fight against a most common problem like pose problem, we are introducing Face Reconstruction System.
Human face detection and recognition is an important technology used in various applications such as video monitor system. Traditional method for taking attendance is Roll Number of student and record the attendance in sheet which takes a lot of time. Because of that systems like automatic attendance is used. To overcome the problems like wastage of time, incorrect attendance, the proposed system gives a method like when he enters the class room , system marks the attendance by extracting the image using Principal Component Analysis algorithm. The system will record the attendance of the student automatically. The student database is collected, it includes name of the students, there images and roll number. It carries an entry in log report of every student of each subject and generates a pdf report of the attendance of the student.
Iris Encryption using (2, 2) Visual cryptography & Average Orientation Circul...AM Publications
Biometric authentication scheme used for person identification. Biometric authentication scheme consists of
uniqueness for identifying human using physiological and behavioral characteristics. So this technique is used for
criminal identification and this technique is used in civil service areas. In order to provide security to the data (2, 2)
secret sharing scheme. Basically iris recognition is the most secured scheme. Visual cryptography is the techniques
that divide the secret into shares.
Facial image classification and searching –a surveyZac Darcy
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, fac
e recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face rec
ognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a margina
l summary for future research and enhancements in face detection
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Graduation Project - Face Login : A Robust Face Identification System for Sec...Ahmed Gad
Face login is my 2015 graduation project started in 2014 and lasted 1.5 years of work.
Generally, it is an identification system using face images. It is a multi-use system but it was mainly created to authorize users to login into their system.
There is an IEEE paper published by the project algorithm used in ICCES 2014 http://ieeexplore.ieee.org/abstract/document/7030929/.
Here is its citation Semary, Noura A., and Ahmed Fawzi Gad. "A proposed framework for robust face identification system." Computer Engineering & Systems (ICCES), 2014 9th International Conference on. IEEE, 2014.
A YouTube video describing the project generally.
https://www.youtube.com/watch?v=OUvaPW70Eko
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
Face recogition from a single sample using rlog filter and manifold analysisacijjournal
Face
recognition is A technique that has been widely used in various important field, this process helps
in
the identification of an individual by a machine for the purpose of security and ease of work. The n
ormal
technique of face recognition usually works bet
ter when there are multiple samples for a single person
(MSSP) is available. In present applications where this technique is to be used such as in social ne
tworks,
security systems, identification cards there is only a single sample per person (SSPP) that
is readily
available. This less availability of the samples causes failure in the working of conventional face
recognition techniques which require multiple samples for a particular individual. To overcome this
drawback which sets back the system from the
accurate functioning of face recognition this paper puts
forward a novel technique which makes use of discriminative multi
-
manifold analysis (DMMA) that
extracts distinctive features using image patches. Recognition is done by the process of manifold to
ma
nifold matching. Hence there is an increment in the accuracy rate of face recognition.
Reduction of False Acceptance Rate Using Cross Validation for Fingerprint Rec...IJTET Journal
Abstract— In the field of biometric modality fingerprint is considered to be one of the most widely used method for individual identity. The fingerprint authentication is used in most application for security purpose. In the biometric systems, the input images are binarized and feature is extraction. The Minutiae matching in fingerprint identification is done by identifying the minutiae point of interest and their relationship. The validation testing in the proposed system using the method of K- fold cross validation by using two , a training set and test set of images to find the appropriate image that matches the input image ,increase the accuracy of recognition by reducing the false acceptance rate of the system.
HUMAN FACE RECOGNITION USING IMAGE PROCESSING PCA AND NEURAL NETWORKijiert bestjournal
Security and authentication of a person is a vital part of any business. There are many techniques use d for this purpose. One of technique is human face recognition . Human Face recognition is an effective means of authenticating a person. The benefit of this approa ch is that,it enables us to detect changes in the face pattern of an individual to substantial extent. The recognition s ystem can tolerate local variations in the face exp ression of an individual. Hence Human face recognition can be use d as a key factor in crime detection mainly to iden tify criminals. There are several approaches to Human fa ce recognition of which Image Processing Principal Component Analysis (PCA) and Neural Networks have been includ ed in our project. The system consists of a databas e of a set of facial patterns for each individual. The charact eristic features called �eigenfaces� are extracted from the stored images using which the system is trained for subseq uent recognition of new images.
Assessment and Improvement of Image Quality using Biometric Techniques for Fa...ijceronline
Biometrics is broadly used in Forensic, highly secured control access and prison security. By making use of this system one can recognizes a person by determining the authentication by his or her biological and physiological features such as Fingerprint, retina-scan, iris scans and face recognition. The determination of the characteristic function of quality and match scores shows that a careful selection of complimentary sets of quality metrics can provide much more benefit to various benefits of biometric quality. Face recognition is a challenging approach to the image quality analysis and many more security applications. Biometric face recognition is the well known technology which is used by the government and civilian applications such Aadhar cards, Pan cards etc. Face recognition is a Behavioral and physiological feature of a human being. Nowadays the quality of an biometric image is the measure concern. There are many factors which are directly or indirectly affects on the image quality hence improvement in image quality has to be done by making the use of some biometric techniques for face recongnion.This paper presents some important techniques for fake biometric detection and improvement of facial image quality.
AN IMPROVED TECHNIQUE FOR HUMAN FACE RECOGNITION USING IMAGE PROCESSINGijiert bestjournal
Face recognition is a computer application technique for automatically identifying or
verifying a person from a digital image or a video frame source. To do this is by comparing
selected facial features from the digital image and a face dataset. It is basically used in
security systems and can be compared to other biometrics such as fingerprint recognition or
eye, iris recognition systems. The main limitation of the current face recognition system is
that they only detect straight faces looking at the camera. Separate versions of the system
could be trained for each head orientation, and the results can be combined using arbitration
methods similar to those presented here. In earlier work, the face position must be centerlight
position; any lighting effect will affect the system. Similarly the eyes of person must be
open and without glass.
CDS is the criminal face identification by capsule neural network.
Solving the common problems in image recognition such as illumination problem, scale variability, and to fight against a most common problem like pose problem, we are introducing Face Reconstruction System.
Human face detection and recognition is an important technology used in various applications such as video monitor system. Traditional method for taking attendance is Roll Number of student and record the attendance in sheet which takes a lot of time. Because of that systems like automatic attendance is used. To overcome the problems like wastage of time, incorrect attendance, the proposed system gives a method like when he enters the class room , system marks the attendance by extracting the image using Principal Component Analysis algorithm. The system will record the attendance of the student automatically. The student database is collected, it includes name of the students, there images and roll number. It carries an entry in log report of every student of each subject and generates a pdf report of the attendance of the student.
Iris Encryption using (2, 2) Visual cryptography & Average Orientation Circul...AM Publications
Biometric authentication scheme used for person identification. Biometric authentication scheme consists of
uniqueness for identifying human using physiological and behavioral characteristics. So this technique is used for
criminal identification and this technique is used in civil service areas. In order to provide security to the data (2, 2)
secret sharing scheme. Basically iris recognition is the most secured scheme. Visual cryptography is the techniques
that divide the secret into shares.
Facial image classification and searching –a surveyZac Darcy
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, fac
e recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face rec
ognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a margina
l summary for future research and enhancements in face detection
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Graduation Project - Face Login : A Robust Face Identification System for Sec...Ahmed Gad
Face login is my 2015 graduation project started in 2014 and lasted 1.5 years of work.
Generally, it is an identification system using face images. It is a multi-use system but it was mainly created to authorize users to login into their system.
There is an IEEE paper published by the project algorithm used in ICCES 2014 http://ieeexplore.ieee.org/abstract/document/7030929/.
Here is its citation Semary, Noura A., and Ahmed Fawzi Gad. "A proposed framework for robust face identification system." Computer Engineering & Systems (ICCES), 2014 9th International Conference on. IEEE, 2014.
A YouTube video describing the project generally.
https://www.youtube.com/watch?v=OUvaPW70Eko
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
Face recogition from a single sample using rlog filter and manifold analysisacijjournal
Face
recognition is A technique that has been widely used in various important field, this process helps
in
the identification of an individual by a machine for the purpose of security and ease of work. The n
ormal
technique of face recognition usually works bet
ter when there are multiple samples for a single person
(MSSP) is available. In present applications where this technique is to be used such as in social ne
tworks,
security systems, identification cards there is only a single sample per person (SSPP) that
is readily
available. This less availability of the samples causes failure in the working of conventional face
recognition techniques which require multiple samples for a particular individual. To overcome this
drawback which sets back the system from the
accurate functioning of face recognition this paper puts
forward a novel technique which makes use of discriminative multi
-
manifold analysis (DMMA) that
extracts distinctive features using image patches. Recognition is done by the process of manifold to
ma
nifold matching. Hence there is an increment in the accuracy rate of face recognition.
Reduction of False Acceptance Rate Using Cross Validation for Fingerprint Rec...IJTET Journal
Abstract— In the field of biometric modality fingerprint is considered to be one of the most widely used method for individual identity. The fingerprint authentication is used in most application for security purpose. In the biometric systems, the input images are binarized and feature is extraction. The Minutiae matching in fingerprint identification is done by identifying the minutiae point of interest and their relationship. The validation testing in the proposed system using the method of K- fold cross validation by using two , a training set and test set of images to find the appropriate image that matches the input image ,increase the accuracy of recognition by reducing the false acceptance rate of the system.
HUMAN FACE RECOGNITION USING IMAGE PROCESSING PCA AND NEURAL NETWORKijiert bestjournal
Security and authentication of a person is a vital part of any business. There are many techniques use d for this purpose. One of technique is human face recognition . Human Face recognition is an effective means of authenticating a person. The benefit of this approa ch is that,it enables us to detect changes in the face pattern of an individual to substantial extent. The recognition s ystem can tolerate local variations in the face exp ression of an individual. Hence Human face recognition can be use d as a key factor in crime detection mainly to iden tify criminals. There are several approaches to Human fa ce recognition of which Image Processing Principal Component Analysis (PCA) and Neural Networks have been includ ed in our project. The system consists of a databas e of a set of facial patterns for each individual. The charact eristic features called �eigenfaces� are extracted from the stored images using which the system is trained for subseq uent recognition of new images.
Assessment and Improvement of Image Quality using Biometric Techniques for Fa...ijceronline
Biometrics is broadly used in Forensic, highly secured control access and prison security. By making use of this system one can recognizes a person by determining the authentication by his or her biological and physiological features such as Fingerprint, retina-scan, iris scans and face recognition. The determination of the characteristic function of quality and match scores shows that a careful selection of complimentary sets of quality metrics can provide much more benefit to various benefits of biometric quality. Face recognition is a challenging approach to the image quality analysis and many more security applications. Biometric face recognition is the well known technology which is used by the government and civilian applications such Aadhar cards, Pan cards etc. Face recognition is a Behavioral and physiological feature of a human being. Nowadays the quality of an biometric image is the measure concern. There are many factors which are directly or indirectly affects on the image quality hence improvement in image quality has to be done by making the use of some biometric techniques for face recongnion.This paper presents some important techniques for fake biometric detection and improvement of facial image quality.
AN IMPROVED TECHNIQUE FOR HUMAN FACE RECOGNITION USING IMAGE PROCESSINGijiert bestjournal
Face recognition is a computer application technique for automatically identifying or
verifying a person from a digital image or a video frame source. To do this is by comparing
selected facial features from the digital image and a face dataset. It is basically used in
security systems and can be compared to other biometrics such as fingerprint recognition or
eye, iris recognition systems. The main limitation of the current face recognition system is
that they only detect straight faces looking at the camera. Separate versions of the system
could be trained for each head orientation, and the results can be combined using arbitration
methods similar to those presented here. In earlier work, the face position must be centerlight
position; any lighting effect will affect the system. Similarly the eyes of person must be
open and without glass.
Innovative Analytic and Holistic Combined Face Recognition and Verification M...ijbuiiir1
Automatic recognition and verification of human faces is a significant problem in the development and application of Human Computer Interaction (HCI).In addition, the demand for reliable personal identification in computerized access control has resulted in an increased interest in biometrics to replace password and identification (ID) card. Over the last couple of years, face recognition researchers have been developing new techniques fuelled by the advances in computer vision techniques, Design of computers, sensors and in fast emerging face recognition systems. In this paper, a Face Recognition and Verification System has been designed which is robust to variations of illumination, pose and facial expression but very sensitive to variations of the features of the face. This design reckons in the holistic or global as well as the analyticor geometric features of the face of the human beings. The global structure of the human face is analysed by Principal Component Analysis while the features of the local structure are computed considering the geometric features of the face such as the eyes, nose and the mouth. The extracted local features of the face are trained and later tested using Artificial Neural Network (ANN). This combined approach of the global and the local structure of the face image is proved very effective in the system we have designed as it has a correct recognition rate of over 90%.
The foundations for biometrics or identification systems were laid long ago. Today these developments have contributed to the identification of people, access to private sites and all places that need security and order with the help of computerized computers that perform biometric facial recognition, exclusively based on images of human faces for their function. With the extraction of facial midst characteristics of each person provides information used for the detection of the face. This communication also addresses the different processes, stages and methods of feature extraction operated by facial recognition systems. Including the positive and negative aspects of the implementation of these, the advantages and disadvantages, peoples criteria in this respect. Tovbaev Sirojiddin | Karshiboev Nizomiddin "Image Based Facial Recognition" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31330.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31330/image-based-facial-recognition/tovbaev-sirojiddin
Profile Identification through Face Recognitionijtsrd
This project is Profile identification through facial recognition system using machine learning, based on K neighbors algorithm. The K neighbours algorithm has high detection rate and fast processing time. Once the face is detected, feature extraction on the face is performed using histogram of oriented gradients which essentially stores the edges of the face as well as the directionality of those edges. Histogram of oriented gradients is an effective form of feature extraction due its high performance in normalizing local contrast. Lastly, training and classification of the facial databases is done where each unique face in the facial database is a class. We attempt to use this facial recognition system on two sets of databases and will analyse the results and then provide the profile of an individual which is written in the Data base created in Firebase. Mr. B. Ravinder Reddy | V. Akhil | G. Sai Preetham | P. Sai Poojitha ""Profile Identification through Face 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/ijtsrd23439.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/23439/profile-identification-through-face-recognition/mr-b-ravinder-reddy
Fake Multi Biometric Detection using Image Quality Assessmentijsrd.com
In the recent era where technology plays a prominent role, persons can be identified (for security reasons) based on their behavioral and physiological characteristics (for example fingerprint, face, iris, key-stroke, signature, voice, etc.) through a computer system called the biometric system. In these kinds of systems the security is still a question mark because of various intruders and attacks. This problem can be solved by improving the security using some efficient algorithms available. Hence the fake person can be identified if he/she uses any synthetic sample of an authenticated person and a fake person who is trying to forge can be identified and authenticated.
Humans often use faces to recognize individuals, and advancements in computing capability over the past few decades now enable similar recognitions automatically. Early facial recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. Major advancements and initiatives in the past 10 to 15 years have propelled facial recognition technology into the spotlight. Facial recognition can be used for both verification and identification.
Face Recognition Based Attendance System with Auto Alert to Guardian using Ca...ijtsrd
Now a days the wise attending management system victimization face detection techniques. Daily attending marking could also be a typical and vital activity in colleges and colleges for checking the performance of students. Manual attending maintaining is tough methodology, significantly for large cluster of students. Some machine driven systems developed to x beat these difficulties, have drawbacks like worth, faux attending, accuracy, meddlesomeness. To beat these drawbacks, there is need of good and automatic attending system. We've a bent to unit implementing attending system victimization face recognition. Since face is exclusive identity of person, the problem of pretend attending and proxies could also be resolved. The system uses native binary pattern face recognition technique because it is fast, straightforward and has larger success rate. Also, its pro vision to have an effect on intensity of sunshine draw back and head produce draw back that produces it effective. This wise system could also be degree effective because of maintain the degree will less squat recognition system is planned supported appearance based choices that concentrate on the shortened squatter image rather than native countenance. The remainder step in squatter recognition system is squatter detection Viola Jones squatter detection methodology that capable of method photos terribly whereas achieving higher detection rates is utilized. The complete squatter recognition methodology could also be divided into a pair of parts squatter detection and squatter identification. For face detection, Viola Jones face detection methodology has been used out of the many face detection ways that. Once face detection, face is cropped from the actual image to urge obviate the background. Chemist faces and shear faces ways that are used for face identification. Average photos of subjects area unit used as coaching job set to spice up the accuracy of identification. Diksha Ghare | Prajakta Katakdhod | Shraddha Ujgare | Komal Suskar | Prof. Amruta Surana ""Face Recognition Based Attendance System with Auto Alert to Guardian using Call and SMS"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23928.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23928/face-recognition-based-attendance-system-with-auto-alert-to-guardian-using-call-and-sms/diksha-ghare
Facial Image Classification And Searching - A SurveyZac Darcy
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, face recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face recognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a marginal summary for future research and enhancements in face detection.
Globally, the presence of biometrics is highly approachable to fix any hurdle and irrelevant input and make a secure and tangible environment. Indeed biometrics helps you tremendously. You can manage everything on your basis to compete in the market. Especially for the attendance services in any organization, office, and building, it is the most important thing to record the presence of someone.
Similar to Scale Invariant Feature Transform Based Face Recognition from a Single Sample per Person (20)
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
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As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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Mission to Decommission: Importance of Decommissioning Products to Increase E...
Scale Invariant Feature Transform Based Face Recognition from a Single Sample per Person
1. ISSN (e): 2250 – 3005 || Vol, 04 || Issue, 10 || October– 2014 ||
International Journal of Computational Engineering Research (IJCER)
www.ijceronline.com Open Access Journal Page 41
Scale Invariant Feature Transform Based Face Recognition from a Single Sample per Person R.Pavithra1, Prof. A. Usha Ruby 2, Dr. J. George Chellin Chandran3 Dept of PG CSE, CSI College of Engineering, Ketti, The Nilgiris, India1 Research scholar, Bharath University, Chennai, India2
I. INTRODUCTION
"Biometrics" means "life measurement" but the term is usually associated with the use of unique physiological characteristics to identify an individual. The application which most people associate with biometrics is security. However, biometric identification has eventually a much broader relevance as computer interface becomes more natural. Knowing the person with whom you are conversing is an important part of human interaction and one expects computers of the future to have the same capabilities. A number of biometric traits have been developed and are used to authenticate the person's identity. The idea is to use the special characteristics of a person to identify him. By using special characteristics we mean the using the features such as face, iris, fingerprint, signature etc, this method of identification based on biometric characteristics is preferred over traditional passwords and PIN based methods for various reasons such as: The person to be identified is required to be physically present at the time-of-identification. Identification based on biometric techniques obviates the need to remember a password or carry a token. A biometric system is essentially a pattern recognition system which makes a personal identification by determining the authenticity of a specific physiological or behavioral characteristic possessed by the user. Biometric technologies are thus defined as the "automated methods of identifying or authenticating the identity of a living person based on a physiological or behavioral characteristic". A biometric system can be either an 'identification' system or a 'verification' (authentication) system, which are defined below. Identification - One to Many: Biometrics can be used to determine a person's identity even without his knowledge or consent. For example, scanning a crowd with a camera and using face recognition technology, one can determine matches against a known database.
ABSTRACT
The technological growth has a serious impact on security which has its own significance. The core objective of this project is to extract the facial features using the local appearance based method for the accurate face identification with single sample per class .The face biometric based person identification plays a major role in wide range of applications such as Airport security, Driver’s license, Passport, Voting System, Surveillance. This project presents face recognition based on granular computing and robust feature extraction using Scale Invariant Feature Transform (SIFT) approach. The Median filter is used to extract the hybrid features and the pyramids are generated after the face granulation. Then, DoG pyramid will be formed from successive iterations of Gaussian images. By this granulation, facial features are segregated at different resolutions to provide edge information, noise, smoothness and blurriness present in a face image. In feature extraction stage, SIFT descriptor utilized to assign the intersecting points which are invariant to natural distortions. This feature is useful to distinguish the maximum number of samples accurately and it is matched with already stored original face samples for identification. The simulated results will be shown used granulation and feature descriptors has better discriminatory power and recognition accuracy in the process of recognizing different facial appearance.
INDEX TERMS: Single sample per class, Median Filter, Dog pyramid, Scale Invariant Feature Transform
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Verification : One to One: Biometrics can also be used to verify a person's identity. For example, one can grant physical access to a secure area in a building by using finger scans or can grant access to a bank account at an ATM by using retinal scan. The process of receiving and analyzing visual information by the human species is referred to as sight, perception or understanding. Similarly, the process of receiving and analyzing visual information by digital computer is called as digital image processing and scene analysis. Processing of an image includes improvement in its appearance and efficient representation. So the field consists of not only feature extraction, analysis and recognition of images, but also coding, filtering, enhancement and restoration. The entire process of image processing and analysis starts from the receiving of the visual information and ends in giving out of description of the scene. The major components of the image processing system are image sensor, digitizer, processor, display unit and storage unit.
Face Recognition-A Survey : A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. Face recognition technology is used to extract information from facial images with the help of a face recognition device, without any human interaction. Unlike face detection technology, face recognition technology uses image processing algorithms to recognize, and then compare human facial images with the ones that are stored in the database of face recognition device. Face recognition technology enabled device analyzes the characteristics of overall structure of human face, including width of nose, shape of cheekbones, width between nose and jaw edges, and distance between eyes. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. These features are then used to search for other images with matching features. Other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image that is useful for face recognition. A probe image is then compared with the face data. One of the earliest successful systems is based on template matching techniques applied to a set of salient facial features, providing a sort of compressed face representation. Recognition algorithms [1, 3,5, 10, 12] can be divided into two main approaches, geometric, which look at distinguishing features, or photometric, which is a statistical approach that distills an image into values and compares the values with templates to eliminate variances. Popular recognition algorithms include Principal Component Analysis using Eigen faces [1, 3], Linear Discriminate Analysis [5], Elastic Bunch Graph Matching using the Fisher face algorithm, the Hidden Markov model, the Multi linear Subspace Learning using tensor representation.
II. EXISTING SYSTEM
Discriminant analysis methods are tools for face recognition. But this is not be used for the single sample per person scenario because of “with- in subject variability”. This variability is established using images in the generic training set for which more than one sample per person is available. When images are under drastic facial expression variation, the discriminant analysis method can’t be used. LDA (Linear Discriminant Analysis) [5] is one of the discriminant analysis methods. It fails to improve the performance, because the complex distribution of the data set is caused by large intrapersonal variations, which still exist in each cluster of that method. LDA uses a Fisher face [1,5] algorithm. It uses the dataset to store multiple images (with- in variability) of a same person. PCA (Principal Component Analysis) [2] is also not suitable for the single sample per class problem. LBP (Local Binary Pattern) [12] is not only used for face detection but also for face recognition. But LBP is not a robust face finder. To digitally process an image, it is first necessary to reduce the image to a series of numbers that can be manipulated by the computer. Each number representing the brightness value of the image at a particular location is called a picture element, or pixel. A typical digitized image may have 512 × 512 or roughly 250,000 pixels, although much larger images are becoming common. Once the image has been digitized, there are three basic operations that can be performed on it in the computer. For a point operation, a pixel value in the output image depends on a single pixel value in the input image. For local operations, several neighboring pixels in the input image determine the value of an output image pixel. In a global operation, all of the input image pixels contribute to an output image pixel value. An image is enhanced when it is modified so that the information it contains is more clearly evident, but enhancement can also include making the image more visually appealing. There are two popular approaches to face recognition. One approach transforms face images into specific transformation domains. Among the works that appear in the literature are Eigen face, Gabor filters, Fourier Transform, and wavelets. Another approach is to extract principal lines and creases from the face. However, this method is not easy because it is sometimes difficult to extract the line structures that can discriminate every individual well.
From the literature survey, it had been inferred that local matching method will be more efficient comparing to holistic matching method. Hence we can eliminate the high dimensionality problem. It had been found that
3. Scale Invariant Feature Transform Based Face…
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multiple samples per class lead to increase space complexity. In order avoid this space complexity problem, single sample per class is considered. In this single sample per class single image of a person is used for matching the face image.
III. PROPOSED SYSTEM
The proposed system use a robust face finder called SIFT(Scale Invariant Feature Transform). By using this feature the face can be fluently detected and recognized. Although only one classifier is trained, and using that frontal, occluded and profile faces are detected. The proposed system includes Four modules: (1) Image Preprocessing, (2) Feature extraction, (3) face granulation, (4)Face recognition or face identification.
Fig 1 Detailed Diagram for Proposed System The first stage of recognition starts with face detection module will be used to obtain face images, which have normalized intensity, are uniform in size and shape and depict only the face region. Here granular computing and face features will be presented to match face images in various illumination changes. The Gaussian operator generates a sequence of low pass filtered images by iteratively convolving each of the constituent images with a 2-D Gaussian kernel. Then, DOG pyramid will be formed from successive iterations of Gaussian images. By this granulation, facial features are segregated at different resolutions to provide edge information, noise, smoothness and blurriness present in a face image. In feature extraction stage, SIFT descriptor utilized to assign the intersecting points which are invariant to natural distortions. This feature is useful to distinguish the maximum number of samples accurately and it is matched with already stored original face samples for identification. Granular Computing & DoG : After digital image has been obtained, the next step deals with preprocessing that image. The key function of preprocessing is to improve the image in ways that increase the chances for success of the other processes. Typically preprocessing deals with techniques for segregating at different resolutions to provide edge information, noise, smoothness and blurriness present in a face image. Subsequently the face granulation is done to generate the Difference of Gaussian (DoG) pyramids to recognize the face by using the granular computing and face features will be presented to match face images in various illumination changes. The Gaussian operator generates a sequence of low pass filtered images by iteratively convolving each of the constituent images with a 2-D Gaussian kernel. Then, DOG pyramid will be formed from successive iterations of Gaussian images. Face Recognition : Recognition is the process that assigns a mark to an object depends on the information provided by its descriptors. Having extracted the hybrid features in the feature extraction step, the Face granulation is done to produce out the Difference of Gaussian (DoG) pyramid formation. The DoG pyramids are produced to analyze the Euclidean distance to discern the face images valiantly
Input Face Image
Median
Filter
Face Region Extraction
DoG Based Face Granulation
Feature
Matching With Database
Person Known/ Unknown
Scale Invariant Feature Transform
4. Scale Invariant Feature Transform Based Face…
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Fig 2 Face Granulation Values The above Fig 2 gives us the clearest idea of getting out the output values based upon the identification of face vector values along with the different usage of granules computed and taken as successive outcomes. The performance is to be measured based on the comparison with the normal input image and the classified granules computed image. IV ALGORITHMS 4.1 Algorithm for Difference of Gaussian (DoG)
Difference of Gaussians is a feature enhancement algorithm that involves the subtraction of one blurred version of an original image from another, less blurred version of the original. In the simple case of grayscale images, the blurred images are obtained by convolving the original grayscale images with Gaussian kernels having differing standard deviations. Blurring an image using a Gaussian kernel suppresses only high-frequency spatial information. Subtracting one image from the other preserves spatial information that lies between the ranges of frequencies that are preserved in the two blurred images. Thus, the difference of Gaussians is a band- pass filter that discards all but a handful of spatial frequencies that are present in the original gray scale image. i)MATHEMATICS OF DIFFERENCE OF GAUSSIANS Given a m-channels, n-dimensional image I : {} {} --------------(1) The difference of Gaussians (DoG) of the image I is the function : {} {} --------(2)
Obtained by subtracting the image I convolved with the Gaussian of variance from the image I convolved with a Gaussian of narrower variance , with>. In one dimension, is defined as: (x) = - ------------- (3) and for the centered two-dimensional case : (x,y)= - ------------ (4)
5. Scale Invariant Feature Transform Based Face…
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Which is formally equivalent to: (x,y)= - ---------------- (5)
Which represents an image convoluted to the difference of two Gaussians, which approximates a Mexican Hat function. As a feature enhancement algorithm, the difference of Gaussians can be utilized to increase the visibility of edges and other detail present in a digital image. The difference of Gaussians algorithm removes high frequency detail that often includes random noise, rendering this approach one of the most suitable for processing images with a high degree of noise. Differences of Gaussians have also been used for blob detection in the scale-invariant feature transform. In fact, the DoG [11] as the difference of two Multivariate normal distribution has always a total null sum and convolving it with a uniform signal generates no response. It approximates well a second derivate of Gaussian (Laplacian of Gaussian) with K~1.6 and the receptive fields of ganglion cells in the retina with K~5. It may easily be used in recursive schemes and is used as an operator in real-time algorithms for blob detection and automatic scale selection. 4.2 Scale Invariant Feature Transform(SIFT) : It generates image features, “key points invariant to image scaling and rotation partially invariant to change in illumination [14].For any object there are many features, interesting points on the object that can be extracted to provide a "feature" description of the object. SIFT image features provide a set of features of an object that are not affected by many of the complications experienced in other methods, such as object scaling and rotation. While allowing for an object to be recognized in a larger image SIFT image features also allow for objects in multiple images of the same location, taken from different positions within the environment, to be recognized. SIFT features are also very resilient to the effects of "noise" in the image. To aid the extraction of these features the SIFT algorithm applies a 4 stage filtering approach: i)Scale-Space Extreme Detection : This stage of the filtering attempts to identify those locations and scales that is identifiable from different views of the same object. This can be efficiently achieved using a "scale space" function. Further it has been shown under reasonable assumptions it must be based on the Gaussian function. The scale space is defined by the function: L(x, y, σ) = G(x, y, σ) * I(x, y) Where * is the convolution operator, G(x, y, σ) is a variable-scale Gaussian and I(x, y) is the input image. Difference of Gaussians is one such technique, locating scale-space extreme, D(x, y, σ) by computing the difference between two images, one with scale k times the other. D(x, y, σ) is then given by: D(x, y, σ) = L(x, y, kσ) - L(x, y, σ) To detect the local maxima and minima of D(x, y, σ) each point is compared with its 8 neighbors at the same scale, and its 9 neighbors up and down one scale. If this value is the minimum or maximum of all these points then this point is an extreme. ii) Key point Localization : This stage attempts to eliminate more points from the list of key points by finding those that have low contrast or are poorly localized on an edge. It involves three main steps. They are a) Interpolation of nearby data for accurate position : First, for each candidate key point, interpolation of nearby data is used to accurately determine its position. The initial approach was to just locate each key point at the location and scale of the candidate key point. The new approach calculates the interpolated location of the extreme, which substantially improves matching and stability. b) Discarding low-contrast key points : To discard the key points with low contrast, the value of the second- order Taylor expansion D(x) is computed at the offset x. If this value is less than 0.03, the candidate key point is discarded. c) Eliminating edge responses : The DoG function will have strong responses along edges, even if the candidate key point is not robust to small amounts of noise.
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For poorly defined peaks in the DoG function, the principal curvature across the edge would be much larger than the principal curvature along it. Finding these principal curvatures amounts to solving for the eigenvalues of the second-order Hessian matrix, H:
H= Dxx Dxy Dxy Dyy iii)ORIENTATION ASSIGNMENT This step aims to assign a consistent orientation to the key points based on local image properties. The key point descriptor can then be represented relative to this orientation, achieving invariance to rotation. Fig 3 SIFT Calculating Values iv)Key point Descriptor : The local gradient data, used above, is also used to create key point descriptors. The gradient information is rotated to line up with the orientation of the key point and then weighted by a Gaussian with variance of 1.5 * key point scale. This data is then used to create a set of histograms over a window centered on the key point. Key point descriptors typically uses a set of 16 histograms, aligned in a 4x4 grid, each with 8 orientation bins, one for each of the main compass directions and one for each of the mid-points of these directions. This result in a feature vector containing 128 elements. These resulting vectors are known as SIFT keys and are used in a nearest-neighbors approach to identify possible objects in an image. V EXPERIMENTAL RESULTS In this section we have evaluated the performance of Difference of Gaussian and feature extraction based on Scale Invariant Feature Transform. Here we test the proposed approach using FERET dataset for face recognition. The image is cropped and made into 64X64 from middle of location of eyes. Here we have considered the local features such as eyes, nose, mouth and chin and also detects properties of relations (e.g. areas, distances, angles) between the features are used as descriptors for face recognition. We have considered 1sample per person for each individual person. Here we will store each person’s image in the FERET database and later which can be used for matching. FERET database is a standard database which is used for storing images. And the resolution of image is 128X128.The results shows that the combined features are useful to distinguish the maximum number of samples accurately and it is matched with already stored original face samples for identification. The results produced by using this method provide better discriminatory power for recognizing different facial appearance with accurate results.