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Journal ArticleDOI

An Efficient Hybrid Face Recognition Algorithm Using PCA and GABOR Wavelets

09 Apr 2014-International Journal of Advanced Robotic Systems (SAGE Publications)-Vol. 11, Iss: 4, pp 59
TL;DR: This work presents a computationally efficient hybrid face recognition method that employs dual-stage holistic and local feature-based recognition algorithms, and obtains better recognition results under illumination variations not only in terms of computation time but also interms of the recognition rate.
Abstract: With the rapid development of computers and the increasing, mass use of high-tech mobile devices, vision-based face recognition has advanced significantly. However, it is hard to conclude that the performance of computers surpasses that of humans, as humans have generally exhibited better performance in challenging situations involving occlusion or variations. Motivated by the recognition method of humans who utilize both holistic and local features, we present a computationally efficient hybrid face recognition method that employs dual-stage holistic and local feature-based recognition algorithms. In the first coarse recognition stage, the proposed algorithm utilizes Principal Component Analysis (PCA) to identify a test image. The recognition ends at this stage if the confidence level of the result turns out to be reliable. Otherwise, the algorithm uses this result for filtering out top candidate images with a high degree of similarity, and passes them to the next fine recognition stage where Gabor filte...
Citations
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Journal ArticleDOI
07 Jan 2020-Sensors
TL;DR: This survey is to review some well-known techniques for each approach and to give the taxonomy of their categories and a solid discussion is given about future directions in terms of techniques to be used for face recognition.
Abstract: Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

257 citations


Cites background or methods from "An Efficient Hybrid Face Recognitio..."

  • ...[99] proposed a computationally efficient hybrid face recognition system that employs both holistic and local features....

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  • ...[99] PCA–LGBPHS Extended Yale Face Bhattacharyya distance Illumination condition Complexity 95% PCA–GABOR Wavelets...

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Journal ArticleDOI
Tianpeng Feng1, Lian Zou1, Jia Yan1, Wenxuan Shi1, Yifeng Liu1, Cien Fan1, Dexiang Deng1 
TL;DR: A hardware accelerated algorithm based on a small-scale over-completed dictionary (SSOCD) via sparse coding (SC) method, which is realized on a parallel hardware platform (TMS320C6678) and shows that the proposed algorithm can run with high parallel efficiency and meets the real-time requirements of industrial inspection.
Abstract: An auto fabric defect detection system via computer vision is used to replace manual inspection. In this paper, we propose a hardware accelerated algorithm based on a small-scale over-completed dictionary (SSOCD) via sparse coding (SC) method, which is realized on a parallel hardware platform (TMS320C6678). In order to reduce computation, the image patches projections in the training SSOCD are taken as features and the proposed features are more robust, and exhibit obvious advantages in detection results and computational cost. Furthermore, we introduce detection ratio and false ratio in order to measure the performance and reliability of the hardware accelerated algorithm. The experiments show that the proposed algorithm can run with high parallel efficiency and that the detection speed meets the real-time requirements of industrial inspection.

128 citations

Journal ArticleDOI
TL;DR: Several applications of a face recognition system such as video surveillance, Access Control, and Pervasive Computing has been discussed and a detailed overview of some important existing methods used to dealing the issues of face recognition have been presented.
Abstract: Face recognition has gained a significant position among most commonly used applications of image processing furthermore availability of viable technologies in this field have contributed a great deal to it. In spite of rapid progress in this field it still has to overcome various challenges like Aging, Partial Occlusion, and Facial Expressions etc affecting the performance of the system, are covered in first part of the survey. This part also highlights the most commonly used databases, available as a standard for face recognition tests. AT & T, AR Database, FERET, ORL and Yale Database have been outlined here. While in the second part of this survey a detailed overview of some important existing methods which are used to dealing the issues of face recognition have been presented. Said methods include Eigenface, Neural Network (NN), Support Vector Machine (SVM), Gabor Wavelet and Hidden Markov Model (HMM). While in last part of the survey several applications of a face recognition system such as video surveillance, Access Control, and Pervasive Computing has been discussed.

82 citations


Cites methods from "An Efficient Hybrid Face Recognitio..."

  • ...[69] 2014 Yale Face Database B PCA, Local Gabor Binary Pattern Histogram Sequence (LGBPHS), DPL PCA 98....

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  • ...Gabor wavelet [69] method is such a method that uses local features for face recognition....

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Journal ArticleDOI
TL;DR: The benefits of, as well as the challenges to the use of face recognition as a biometric tool are exposed, and a detailed survey of some well-known methods by expressing each method’s principle is provided.
Abstract: Despite the existence of various biometric techniques, like fingerprints, iris scan, as well as hand geometry, the most efficient and more widely-used one is face recognition. This is because it is inexpensive, non-intrusive and natural. Therefore, researchers have developed dozens of face recognition techniques over the last few years. These techniques can generally be divided into three categories, based on the face data processing methodology. There are methods that use the entire face as input data for the proposed recognition system, methods that do not consider the whole face, but only some features or areas of the face and methods that use global and local face characteristics simultaneously. In this paper, we present an overview of some well-known methods in each of these categories. First, we expose the benefits of, as well as the challenges to the use of face recognition as a biometric tool. Then, we present a detailed survey of the well-known methods by expressing each method’s principle. After that, a comparison between the three categories of face recognition techniques is provided. Furthermore, the databases used in face recognition are mentioned, and some results of the applications of these methods on face recognition databases are presented. Finally, we highlight some new promising research directions that have recently appeared.

76 citations


Cites methods from "An Efficient Hybrid Face Recognitio..."

  • ...• PCA and Gabor wavelets [85]: This is a new approach that uses a face recognition algorithm with two steps of recognition based on both global and local features....

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Journal ArticleDOI
TL;DR: The aim of this research is to provide comprehensive literature review over face recognition along with its applications and some of the major findings are given in conclusion.
Abstract: With the rapid growth in multimedia contents, among such content face recognition has got much attention especially in past few years. Face as an object consists of distinct features for detection; therefore, it remains most challenging research area for scholars in the field of computer vision and image processing. In this survey paper, we have tried to address most endeavoring face features such as pose invariance, aging, illuminations and partial occlusion. They are considered to be indispensable factors in face recognition system when realized over facial images. This paper also studies state of the art face detection techniques, approaches, viz. Eigen face, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Gabor Wavelets, Elastic Bunch Graph Matching, 3D morphable Model and Hidden Markov Models. In addition to the aforementioned works, we have mentioned different testing face databases which include AT & T (ORL), AR, FERET, LFW, YTF, and Yale, respectively for results analysis. However, aim of this research is to provide comprehensive literature review over face recognition along with its applications. And after in depth discussion, some of the major findings are given in conclusion.

45 citations

References
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Journal ArticleDOI
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Abstract: We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.

14,562 citations


"An Efficient Hybrid Face Recognitio..." refers methods in this paper

  • ...Face recognition based on the Eigenspace algorithm [3-6] - one of the holistic methods - was introduced in 1991 and achieved more noticeable recognition performance than other available algorithms at the time....

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Journal ArticleDOI
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Abstract: We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the eigenface technique for tests on the Harvard and Yale face databases.

11,674 citations


"An Efficient Hybrid Face Recognitio..." refers methods in this paper

  • ...LDA uses the class information of features and finds a set of the most discriminating feature vectors that minimizes within-class scatter while maximizing between-class scatter [8]....

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Journal ArticleDOI
TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
Abstract: As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

6,384 citations

Journal ArticleDOI
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


"An Efficient Hybrid Face Recognitio..." refers background or methods in this paper

  • ...The LBP is an operator which moves a 3x3 window in an image to extract the structural features which are known to be robust to the illumination and noise variations [15]....

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  • ...The LBP exhibits relatively good performance in face detection and recognition, while its operation is simple [15, 28]....

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  • ...[15] applied a texture descriptor, called a ‘Local Binary Pattern’ (LBP), for face recognition....

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Journal ArticleDOI
TL;DR: A generative appearance-based method for recognizing human faces under variation in lighting and viewpoint that exploits the fact that the set of images of an object in fixed pose but under all possible illumination conditions, is a convex cone in the space of images.
Abstract: We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and albedo of the face can be reconstructed. In turn, this reconstruction serves as a generative model that can be used to render (or synthesize) images of the face under novel poses and illumination conditions. The pose space is then sampled and, for each pose, the corresponding illumination cone is approximated by a low-dimensional linear subspace whose basis vectors are estimated using the generative model. Our recognition algorithm assigns to a test image the identity of the closest approximated illumination cone. Test results show that the method performs almost without error, except on the most extreme lighting directions.

5,027 citations


"An Efficient Hybrid Face Recognitio..." refers methods in this paper

  • ...All the experiments in this study were carried out on the database of 2,432 images of 38 individuals from the Extended Yale Face Database B [31, 32]....

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