491,020 research outputs found
A survey of face recognition techniques under occlusion
The limited capacity to recognize faces under occlusions is a long-standing
problem that presents a unique challenge for face recognition systems and even
for humans. The problem regarding occlusion is less covered by research when
compared to other challenges such as pose variation, different expressions,
etc. Nevertheless, occluded face recognition is imperative to exploit the full
potential of face recognition for real-world applications. In this paper, we
restrict the scope to occluded face recognition. First, we explore what the
occlusion problem is and what inherent difficulties can arise. As a part of
this review, we introduce face detection under occlusion, a preliminary step in
face recognition. Second, we present how existing face recognition methods cope
with the occlusion problem and classify them into three categories, which are
1) occlusion robust feature extraction approaches, 2) occlusion aware face
recognition approaches, and 3) occlusion recovery based face recognition
approaches. Furthermore, we analyze the motivations, innovations, pros and
cons, and the performance of representative approaches for comparison. Finally,
future challenges and method trends of occluded face recognition are thoroughly
discussed
Discriminant face features extraction, analysis & its application in multipose face recognization: a survey
As one of the excellent learning and
classification performance, SVM and ISVM has become a
research topic in the field of machine learning and has been
applied in many areas, such as face detection and
recognition, handwriting automatic identification and
automatic text categorization. Face recognition is a
challenging computer vision problem. Given a face
database, goal of face recognition is to compare the input
image class with all the classes and then declare a decision
that identifies to whom the input image class belongs to or if
it doesn’t belong to the database at all. In this survey, we
study face recognition as a pattern classification problem.In
this paper, we study the concept of SVM and sophisticated
classification techniques for face recognition using the SVM
and ISVM along with the advantages and disadvantages.
This paper not only provides an up-to-date critical survey of
machine learning techniques but also performance analysis
of various SVM and ISVM techniques for face recognition
are compared
Discriminant face features extraction, analysis & its application in multipose face recognization: a survey
As one of the excellent learning and
classification performance, SVM and ISVM has become a
research topic in the field of machine learning and has been
applied in many areas, such as face detection and
recognition, handwriting automatic identification and
automatic text categorization. Face recognition is a
challenging computer vision problem. Given a face
database, goal of face recognition is to compare the input
image class with all the classes and then declare a decision
that identifies to whom the input image class belongs to or if
it doesn’t belong to the database at all. In this survey, we
study face recognition as a pattern classification problem.In
this paper, we study the concept of SVM and sophisticated
classification techniques for face recognition using the SVM
and ISVM along with the advantages and disadvantages.
This paper not only provides an up-to-date critical survey of
machine learning techniques but also performance analysis
of various SVM and ISVM techniques for face recognition
are compared
FEATURE-BASED FACE DETECTION: A SURVEY
Human and computer vision has a vital role in intelligent interaction with computer, face recognition is one of the subjects that have a wide area in researches, a big effort has been exerted in last decades for face recognition, face detection, face tracking, as yet new algorithms for building fully automated system are required, these algorithms should be robust and efficient. The first step of any face recognition system is face detection, the goal of face detection is the extraction of face region within image, taking into consideration lightning, orientation and pose variation, whenever this step accurate the result of face recognition will be better, this paper introduce a survey of techniques and methods of feature based face detection
A Comprehensive Review on Face detection using Machine learning classification
Increased interest in biometric security systems makes face recognition a very effective research area. Now, we will take a look on basics of a face recognition system. In an image a face region is present where only facial features are present. In another terms it means we have to localize or concentrate only on the face region that means we are contemplating those parts of an image where a face may present. This research provides an up-to date survey of various existing recognition techniques but also represent precise descriptions of some methods. In addition to this other topics like issues of illumination and pose variation are also discussed in their work. The purpose of this short review paper is to present, categorize and evaluate some new face detection techniques using four conventional learning machine. The performance and the other evaluation parameters of these methods compare with each other in order to introduce significant techniques and also to state advantages and disadvantages of related work
A survey of face detection, extraction and recognition
The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important
Human Face Recognition and Age Estimation with Machine Learning: A Critical Review and Future Perspective
Face Recognition (FR) applications are becoming more and more common these days. Face recognition, techniques, tools, and performance are all shown in this work, along with a literature review and gaps in many areas. Some of the most common uses of the FR include medical and government sectors as well as educational institutions. The FR technique can identify an appropriate individual through a camera. Online courses, online FDPs, and Webinars are becoming more interactive nowadays. Using Machine Learning, it is possible to quickly and securely determine a student\u27s unique id to administer virtual online tests. The paper is an analysis of Machine learning and deep learning algorithms as well as tools such as Matlab and Python. The paper covers a survey of different aspects such as face detection, face recognition, face expressions, and age estimation. Hence, this is helpful for researchers to choose the right direction for their research. Future face recognition research is also considered in the paper which is now trending in face recognition systems. Data from recent years are used to evaluate the performance
A Survey on Human Face Recognition Invariant to Illumination”,
ABSTRACT Human face recognition is one of the research areas in the current era of the research. It is one the widely used biometric technique for identification and verification of the human face. There are many challenges to face recognition which degrade the performance of the algorithm. The illumination variation problem is one of the well-known problems in face recognition in uncontrolled environment. In this paper an extensive and up-to-date survey of the existing techniques to address this problem is presented. Different authors have given so many techniques for illumination reduction from the face image but still some combined survey is missing so we have tried to fill that gaps in this paper. We have collected various preprocessing techniques suggested by different authors and shown their results in a tabular form. After preprocessing we can use any of the recognition method for face recognition. There are so many online face databases available so we can use any of them
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