Review of Face Recognition Techniques
17 Apr 2017-International Journal of Computer Applications (Foundation of Computer Science (FCS), NY, USA)-Vol. 164, Iss: 6, pp 31-35
TL;DR: This paper conducts a survey of techniques which are available for face detection and indicates that hybrid approach with discrete wavelet transformation produces better results.
Abstract: Face Recognition is used in order to ensure authentication in terms of feature verification. Techniques are defined to identify faces under different situations. This paper conducts a survey of techniques which are available for face detection. Recognition is possible in case features are extracted from the presented face images. For this purpose feature extraction mechanisms like discrete wavelet transformation (DWT), SIFT, linear discriminate analysis (LDA), principal component analysis (PCA) are commonly used. Analysis process indicates that hybrid approach with discrete wavelet transformation produces better results. Comparative study of literature is also presented through this work.
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TL;DR: It is argued that CapsNet seems to be a promising new technique for image classification, and further experiments using more robust computation resources and re-fined CapsNet architectures may produce better outcomes.
Abstract: Image classification has become one of the main tasks in the field of computer vision technologies. In this context, a recent algorithm called CapsNet that implements an approach based on activity vectors and dynamic routing between capsules may overcome some of the limitations of the current state of the art artificial neural networks (ANN) classifiers, such as convolutional neural networks (CNN). In this paper, we evaluated the performance of the CapsNet algorithm in comparison with three well-known classifiers (Fisher-faces, LeNet, and ResNet). We tested the classification accuracy on four datasets with a different number of instances and classes, including images of faces, traffic signs, and everyday objects. The evaluation results show that even for simple architectures, training the CapsNet algorithm requires significant computational resources and its classification performance falls below the average accuracy values of the other three classifiers. However, we argue that CapsNet seems to be a promising new technique for image classification, and further experiments using more robust computation resources and re-fined CapsNet architectures may produce better outcomes.
86 citations
Cites methods from "Review of Face Recognition Techniqu..."
...This algorithm has proved to be fast, reliable [14,15], and one the most successful methods for face recognition [16,17] achieving an average of 96....
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TL;DR: This review presents the broad range of methods used for face recognition and attempts to discuss their advantages and disadvantages, and presents the possibilities and future implications for further advancing the field.
Abstract: The face is an important part of the human body, distinguishing individuals in large groups of people. Thus, because of its universality and uniqueness, it has become the most widely used and accep...
51 citations
Cites methods from "Review of Face Recognition Techniqu..."
...approaches for face recognition need to be followed and applied for improved face recognition.(28)...
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TL;DR: The face recognition time and attendance system with real-time video processing can quickly complete the tasks of students in theTime and attendance check-in system, get rid of the complex naming phenomenon, greatly improve the efficiency of class, and play an important role in guiding the development of the time and Attendance system.
Abstract: With the advent of the era of big data in the world and the commercial value of face recognition technology, the prospects for face recognition technology are very bright and have great market demand. This article aims to design a face recognition attendance system based on real-time video processing. This article mainly sets four directions to consider the problems: the accuracy rate of the face recognition system in the actual check-in, the stability of the face recognition attendance system with real-time video processing, the truancy rate of the face recognition attendance system with real-time video processing and the interface settings of the face recognition attendance system using real-time video processing. By analyzing the situation of these problems, the concept of attendance system based on face recognition technology is proposed, and the research on face recognition attendance system based on real-time video processing is carried out. Experimental data shows that the accuracy rate of the video face recognition system is up to 82%. Compared with the traditional check-in method, the face recognition attendance system can be reduced by about 60%. The rate of skipping classes has greatly reduced the phenomenon of students leaving early and skipping classes. The face recognition time and attendance system with real-time video processing through the above experimental certification can quickly complete the tasks of students in the time and attendance check-in system, get rid of the complex naming phenomenon, greatly improve the efficiency of class, and play an important role in guiding the development of the time and attendance system.
49 citations
Cites background from "Review of Face Recognition Techniqu..."
...Face recognition technology has many typical applications in the field of public safety, civil economy, and home entertainment [1], [2]....
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TL;DR: Experimental results show that the violence detection method proposed in this paper improves the accuracy of violence detection in video.
28 citations
References
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TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
Abstract: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed
5,563 citations
TL;DR: A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.
Abstract: A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.
4,678 citations
"Review of Face Recognition Techniqu..." refers background in this paper
...Recently, biometric technology has been introduced which allows the true recognition [2] of any individual on the behalf of their physiological and behavioural characteristics [3]....
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...Practical requirements [3] Universality: characteristics should be compulsory for every individual....
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26 Oct 1997
TL;DR: A system for recognizing human faces from single images out of a large database containing one image per person, based on a Gabor wavelet transform, which differs from Lades et al. (1993) in three respects.
Abstract: We present a system for recognizing human faces from single images out of a large database containing one image per person. Faces are represented by labeled graphs, based on a Gabor wavelet transform. Image graphs of new faces are extracted by an elastic graph matching process and can be compared by a simple similarity function. The system differs from Lades et al. (1993) in three respects. Phase information is used for accurate node positioning. Object-adapted graphs are used to handle large rotations in depth. Image graph extraction is based on a novel data structure, the bunch graph, which is constructed from a small set of sample image graphs.
1,843 citations
01 Jan 2004
370 citations
"Review of Face Recognition Techniqu..." refers methods in this paper
...A biometric system is designed using the following four main modules [10] shows in Fig....
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TL;DR: It is described how two-dimensional face images can be converted into one-dimensional sequences to allow similar techniques to be applied and how a HMM can be used to automatically segment face images and extract features that can be use for identification.
343 citations