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Journal ArticleDOI

Two-dimensional PCA: a new approach to appearance-based face representation and recognition

TL;DR: A new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation that is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction.
Abstract: In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.

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Citations
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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

Journal ArticleDOI
Yan, Xu, Zhang, Yang, Lin 
TL;DR: A new supervised dimensionality reduction algorithm called marginal Fisher analysis is proposed in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizing the interclass separability.
Abstract: A large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called marginal Fisher analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional linear discriminant analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions

2,751 citations

Journal ArticleDOI
TL;DR: A new supervised dimensionality reduction algorithm called marginal Fisher analysis is proposed in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizing the interclass separability.
Abstract: A large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called marginal Fisher analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability We show that MFA effectively overcomes the limitations of the traditional linear discriminant analysis algorithm due to data distribution assumptions and available projection directions Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions

2,339 citations


Cites methods from "Two-dimensional PCA: a new approach..."

  • ...2DPCA [29] is a simplified second-order tensorization of PCA and only optimizes one projection direction, while [30] and [25] are full formulations of the second-order tensorization of PCA....

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  • ...Recently, a number of algorithms [25], [26], [27], [29], [30], [31] have been proposed to conduct dimensionality reduction on objects encoded as matrices or tensors of arbitrary order....

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Journal ArticleDOI
TL;DR: The paper focuses on the use of principal component analysis in typical chemometric areas but the results are generally applicable.
Abstract: Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal component analysis. The paper focuses on the use of principal component analysis in typical chemometric areas but the results are generally applicable.

1,622 citations

Journal ArticleDOI
TL;DR: A novel approach of face identification by formulating the pattern recognition problem in terms of linear regression, using a fundamental concept that patterns from a single-object class lie on a linear subspace, and introducing a novel Distance-based Evidence Fusion (DEF) algorithm.
Abstract: In this paper, we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single-object class lie on a linear subspace, we develop a linear model representing a probe image as a linear combination of class-specific galleries. The inverse problem is solved using the least-squares method and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Linear Regression Classification (LRC) algorithm falls in the category of nearest subspace classification. The algorithm is extensively evaluated on several standard databases under a number of exemplary evaluation protocols reported in the face recognition literature. A comparative study with state-of-the-art algorithms clearly reflects the efficacy of the proposed approach. For the problem of contiguous occlusion, we propose a Modular LRC approach, introducing a novel Distance-based Evidence Fusion (DEF) algorithm. The proposed methodology achieves the best results ever reported for the challenging problem of scarf occlusion.

972 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


"Two-dimensional PCA: a new approach..." refers methods in this paper

  • ...Within this context, Turk and Pentland [3] presented the well-known Eigenfaces method for face recognition in 1991....

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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


"Two-dimensional PCA: a new approach..." refers methods in this paper

  • ...The performance of 2DPCA was also compared with other methods, including Fisherfaces [16], ICA [13], [14], and Kernel Eigenfaces [14]....

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01 Jan 1998

3,650 citations


"Two-dimensional PCA: a new approach..." refers background in this paper

  • ...4.2 Experiment on the AR Database The AR face database [ 17 ], [18] contains over 4,000 color face images of 126 people (70 men and 56 women), including frontal views of faces with different facial expressions, lighting conditions and occlusions....

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Journal Article

2,952 citations


"Two-dimensional PCA: a new approach..." refers background in this paper

  • ...The AR face database [17], [18] contains over 4,000 color face images of 126 people (70 men and 56 women), including frontal views of faces with different facial expressions, lighting conditions and occlusions....

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Journal ArticleDOI
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 is constructed from a small get of sample image graphs.
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 the preceding one (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 get of sample image graphs.

2,934 citations


"Two-dimensional PCA: a new approach..." refers background in this paper

  • ...However, Wiskott et al. [ 10 ] pointed out that PCA could not capture even the simplest invariance unless this information is explicitly provided in the training data....

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