Incremental linear discriminant analysis for face recognition. (2008)

by H Zhao, P Yuen
Venue:IEEE T. Syst. Man Cy. B.,
Add To MetaCart
Results 1 - 10 of 136

Face Recognition: A Literature Survey

by W. Zhao, R. Chellappa, P. J. Phillips, A. Rosenfeld , 2000
"... ... 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 ..."
Abstract - Cited by 1398 (21 self) - Add to MetaCart
... 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,

From Few to many: Illumination cone models for face recognition under variable lighting and pose

by Athinodoros S. Georghiades, Peter N. Belhumeur, David J. Kriegman - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2001
"... 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 smal ..."
Abstract - Cited by 754 (12 self) - Add to MetaCart
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 (based on Euclidean distance within the image space). We test our face recognition method on 4050 images from the Yale Face Database B; these images contain 405 viewing conditions (9 poses ¢ 45 illumination conditions) for 10 individuals. The method performs almost without error, except on the most extreme lighting directions, and significantly outperforms popular recognition methods that do not use a generative model.

Locality Preserving Projection,"

by Xiaofei He , Partha Niyogi - Neural Information Processing System, , 2004
"... Abstract Many problems in information processing involve some form of dimensionality reduction. In this paper, we introduce Locality Preserving Projections (LPP). These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the ..."
Abstract - Cited by 414 (16 self) - Add to MetaCart
Abstract Many problems in information processing involve some form of dimensionality reduction. In this paper, we introduce Locality Preserving Projections (LPP). These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set. LPP should be seen as an alternative to Principal Component Analysis (PCA) -a classical linear technique that projects the data along the directions of maximal variance. When the high dimensional data lies on a low dimensional manifold embedded in the ambient space, the Locality Preserving Projections are obtained by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold. As a result, LPP shares many of the data representation properties of nonlinear techniques such as Laplacian Eigenmaps or Locally Linear Embedding. Yet LPP is linear and more crucially is defined everywhere in ambient space rather than just on the training data points. This is borne out by illustrative examples on some high dimensional data sets.

Face recognition using laplacianfaces

by Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, Hong-jiang Zhang - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2005
"... Abstract—We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) wh ..."
Abstract - Cited by 389 (38 self) - Add to MetaCart
Abstract—We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition. Index Terms—Face recognition, principal component analysis, linear discriminant analysis, locality preserving projections, face manifold, subspace learning. 1
(Show Context)

KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition

by Jian Yang, Alejandro F. Frangi, Jing-yu Yang, David Zhang, Zhong Jin - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2005
"... This paper examines the theory of kernel Fisher discriminant analysis (KFD) in a Hilbert space and develops a two-phase KFD framework, i.e., kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). This framework provides novel insights into the nature of KFD. Base ..."
Abstract - Cited by 139 (7 self) - Add to MetaCart
This paper examines the theory of kernel Fisher discriminant analysis (KFD) in a Hilbert space and develops a two-phase KFD framework, i.e., kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). This framework provides novel insights into the nature of KFD. Based on this framework, the authors propose a complete kernel Fisher discriminant analysis (CKFD) algorithm. CKFD can be used to carry out discriminant analysis in “double discriminant subspaces.” The fact that, it can make full use of two kinds of discriminant information, regular and irregular, makes CKFD a more powerful discriminator. The proposed algorithm was tested and evaluated using the FERET face database and the CENPARMI handwritten numeral database. The experimental results show that CKFD outperforms other KFD algorithms.
(Show Context)

Efficient and Robust Feature Extraction by Maximum Margin Criterion

by Haifeng Li, Tao Jiang, Keshu Zhang - In Advances in Neural Information Processing Systems 16 , 2003
"... In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two most popular linear dimen-sionality reduction methods. Howev ..."
Abstract - Cited by 116 (5 self) - Add to MetaCart
In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two most popular linear dimen-sionality reduction methods. However, PCA is not very effective for the extraction of the most discriminant features and LDA is not stable due to the small sample size problem. In this pa-per, we propose some new (linear and nonlinear) feature extractors based on maximum margin criterion (MMC). Geometrically, feature extractors based on MMC maximize the (average) margin between classes after dimensionality reduction. It is shown that MMC can represent class separability better than PCA. As a connection to LDA, we may also derive LDA from MMC by incorporating some constraints. By using some other constraints, we establish a new linear feature extractor that does not suffer from the small sample size problem, which is known to cause serious stability problems for LDA. The kernelized (nonlinear) counterpart of this lin-ear feature extractor is also established in the paper. Our extensive experiments demonstrate that the new feature extractors are effective, stable, and efficient.
(Show Context)

The CAS-PEAL large-scale Chinese face dataBase and baseline evaluations

by Wen Gao, Bo Cao, Shiguang Shan, Delong Zhou, Xiaohua Zhang, Debin Zhao - JOINT RESEARCH & DEVELOPMENT LABORATORY, CAS , 2004
"... The ICT-ISVISION Joint Research & Development Laboratory (JDL) for Face Recognition has constructed the CAS-PEAL face database, supported by National Hi-Tech Program and ISVISION Technologies Co., Ltd. The goals to create the PEAL face database include (1) providing the worldwide researchers of ..."
Abstract - Cited by 108 (7 self) - Add to MetaCart
The ICT-ISVISION Joint Research & Development Laboratory (JDL) for Face Recognition has constructed the CAS-PEAL face database, supported by National Hi-Tech Program and ISVISION Technologies Co., Ltd. The goals to create the PEAL face database include (1) providing the worldwide researchers of FR community a large-scale face database for training and evaluating their algorithms; (2) facilitating the development of FR by providing large-scale face images with different sources of variations, especially Pose, Expression, Accessories, and Lighting (PEAL); (3) advancing the state-of-the-art face recognition technologies aiming at practical applications especially for the oriental. Currently, the CAS-PEAL face database contains 99,594 images of 1040 individuals (595 males and 445 females) with varying Pose, Expression, Accessory, and Lighting (PEAL). For each subject, 9 cameras spaced equally in a horizontal semicircular shelf are used to simultaneously capture images across different poses in one shot. Each subject is also asked to look up and down to capture 18 images in another two shots. We also considered 5 kinds of expressions, 6 kinds of accessories (3 pairs of glasses, and 3 caps), and 15 lighting directions. This face database is now partly made available (a subset named by CAS-PEAL-R1, containing

Face recognition from a single image per person: A survey

by Xiaoyang Tan, Songcan Chen, Zhi-hua Zhou , Fuyan Zhang - PATTERN RECOGNITION , 2006
"... One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples. Fewer samples per person mean less laborious effort for collecting them, lower costs for storing and processing them. Unfortunately, many reported face recognition techniques ..."
Abstract - Cited by 108 (6 self) - Add to MetaCart
One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples. Fewer samples per person mean less laborious effort for collecting them, lower costs for storing and processing them. Unfortunately, many reported face recognition techniques rely heavily on the size and representative of training set, and most of them will suffer serious performance drop or even fail to work if only one training sample per person is available to the systems. This situation is called “one sample per person ” problem: given a stored database of faces, the goal is to identify a person from the database later in time in any different and unpredictable poses, lighting, etc from just one image. Such a task is very challenging for most current algorithms due to the extremely limited representative of training sample. Numerous techniques have been developed to attack this problem, and the purpose of this paper is to categorize and evaluate these algorithms. The prominent algorithms are described and critically analyzed. Relevant issues such as data collection, the influence of the small sample size, and system evaluation are discussed, and several promising directions for future research are also proposed in this paper.
(Show Context)

A Nonparametric Statistical Comparison of Principal Component and Linear Discriminant Subspaces for Face Recognition

by J. Ross Beveridge, Kai She, Bruce A. Draper, Geof H. Givens - In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2001
"... The FERET evaluation compared recognition rates for different semi-automated and automated face recognition algorithms. We extend FERET by considering when differences in recognition rates are statistically distinguishable subject to changes in test imagery. Nearest Neighbor classifiers using princi ..."
Abstract - Cited by 72 (11 self) - Add to MetaCart
The FERET evaluation compared recognition rates for different semi-automated and automated face recognition algorithms. We extend FERET by considering when differences in recognition rates are statistically distinguishable subject to changes in test imagery. Nearest Neighbor classifiers using principal component and linear discriminant subspaces are compared using different choices of distance metric. Probability distributions for algoriithm recognition rates and pairwise differences in recognition rates are determined using a permutation methodology. The principal component subspace with Mahalanobis distance is the best combination; using L2 is second best. Choice of distance measure for the linear discriminant subspace matters little, and performance is always worse than the principal components classifier using either Mahalanobis or L1 distance. We make the source code for the algorithms, scoring procedures and Monte Carlo study available in the hopes others will extend this comparison to newer algorithms.
(Show Context)

SFS Based View Synthesis for Robust Face Recognition

by Wenyi Zhao, Rama Chellappa , 2000
"... Sensitivity to variations in pose is a challenging problem in face recognition using appearance-based methods. More specifically, the appearance of a face changes dramatically when viewing and/or lighting directions change. Various approaches have been proposed to solve this difficult problem. They ..."
Abstract - Cited by 69 (5 self) - Add to MetaCart
Sensitivity to variations in pose is a challenging problem in face recognition using appearance-based methods. More specifically, the appearance of a face changes dramatically when viewing and/or lighting directions change. Various approaches have been proposed to solve this difficult problem. They can be broadly divided into three classes: 1) multiple image based methods where multiple images of various poses per person are available, 2) hybrid methods where multiple example images are available during learning but only one database image per person is available during recognition, and 3) single image based methods where no example based learning is carried out. In this paper, we present a method that comes under class 3. This method based on shape-from-shading (SFS) improves the performance of a face recognition system in handling variations due to pose and illumination via image synthesis.