A. Krishnaswamy W. Zhao, R. Chellappa. Discriminant analysis of principal components for face recognition. Proc. of the 3rd IEEE International Conference on Face and Gesture Recognition(FG), pages 14-- 16, April 1998. Home/Search Document Details and Download
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A Nonparametric Statistical Comparison of Principal.. - Beveridge, She.. (2001) (2 citations) (Correct)
....algorithms based upon PCA that arose from the work by [11, 10] The PCA algorithm used here is for all intents and purposes equivalent to the Eigenface algorithm used in FERET. One of the top performing algorithms in the FERET evaluation was an LDA algorithm developed by Zhao and Chellapa [17]. Of the top performing algorithms in FERET, this is the one based upon the oldest and best understood subspace projection technique after PCA [4, 1] For both these reasons, a similar LDA algorithm has been chosen for our study. Stepping back from face recognition, characterizing the performance ....
....uses the PCA subspace projection as a first step in processing the image data. Thus, the Fisher Linear Discriminants are defined in the # dimensional subspace defined by the first # principal components. This design choice is consistent with prior uses of LDA algorithms to perform face recognition [17]. Fisher s method defines ### basis vectors where # is the number of classes. These basis vectors may be expressed as rows in a matrix # , and the discriminants are defined as those basis vectors that maximize the ratio of distances between classes divided by distances within each class: ##### ....
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W. Zhao, R. Chellappa, and A. Krishnaswamy. Discriminant analysis of principal components for face recognition. In In Wechsler, Philips, Bruce, Fogelman-Soulie, and Huang, editors, Face Recognition: From Theory to Applications, pages 73--85, 1998.
Analysis of LDA-based matching schemes for - Face Verification Li (Correct)
....the selection of measurement and thresholding in LDA subspace plays a supplementary performance significantly. For such a long time since the LDA was employed for face recognition or PIV, the basic matching scheme applied in the LDA subspace is the nearest Euclidean distance matching[19] 9] 10][21][8] In [8] the Euclidean distance classifier exhibited better performance in the properly implemented LDA subspace than that achieved in the PCA subspace. The Euclidean distance was employed in nearly all face recognition experiments using LDA or PCA[ 20] 16] etc] One derived distance measure ....
Wenyi Zhao et al, "Discriminant Analysis of Principal Components for Face Recognition, " In Harry Wechsler et al,editors,Face Recognition From theory to Applications, NATO ASI Series, pp. 73-85, Springer, 1998.
Parametric and Nonparametric Methods for the.. - Beveridge, She.. (2001) (1 citation) (Correct)
....the amount of variation that will be seen in practical circumstances where galleries vary. For many applications, evaluation studies accounting for variation in gallery imagery are more appropriate. Here we present a summary of a Monte Carlo study designed to compare PCA [11, 10] and PCA LDA [15] algorithms under varying choices of gallery and probe images [2] The algorithm descriptions and data preprocessing steps are omitted here, since the exact nature of the algorithms and data is less important than the methodology being illustrated. These details may be found in [2] The study ....
W. Zhao, R. Chellappa, and A. Krishnaswamy. Discriminant analysis of principal components for face recognition. In In Wechsler, Philips, Bruce, Fogelman-Soulie, and Huang, editors, Face Recognition: From Theory to Applications, pages 73--85, 1998.
Constructing Structures of Facial Identities on the View.. - Li, Gong, Liddell (2001) (Correct)
....variance [12] proved to be a more suitable technique for class separation. Computationally, LDA can be solved as an eigen decomposition problem similar to PCA. Swets and Weng [28] applied a subsequent LDA projection followed by PCA to derive the Most Discriminating Features. Zhao et al. [34] used LDA as a representation for frontal view face recognition. Edwards et al. 11] adopted LDA to select discriminant parameters based on Active Ap pearance Models. They argued that these parameters can be used to decouple identity 2 variance from pose, lighting and expression variance. ....
W. Zhao, A. Krishnaswamy, R. Chellappa, D. Swets, and J. Weng. Discriminant analysis of principal components for face recognition. In Wechsler, Philips, Bruce, Fogelman-Soulie, and Huang, editors, Face Recognition: From Theory to Applications, pages 73-85. Springer- Verlag, 1998. 25
Face Detection Using an SVM Trained in Eigenfaces Space - Popovici, Thiran (2003) (Correct)
....decade ago ( 2] 3] and proved its capabilities in different contexts like face detection or face recognition. However, in the case of most applications a simple decision rule, e.g. a simple threshold (like in the case of distance from feature space see below) or a linear classifier (LDA) [4] is used to discriminate between faces and non faces or for face recognition. Another problem is how to choose the number of required principal components. While in the context of Work partially performed in the BANCA project of the IST European program with the financial support of the Swiss ....
Zhao, W., Chellappa, R., Krishaswamy, A.: Discriminant analysis of principal components for face recognition. In: Proceedings of the 3rd International Conference on Automatic Face and Gesture Recognition. (1998)
Human Identification Using Gait and Face - Chellappa, Roy-Chowdhury, Zhou Self-citation (Chellappa) (Correct)
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W. Zhao, R. Chellappa, and A. Krishnaswamy. Discriminant analysis of principal components for face recognition. Proceedings, International Conference on Automatic Face and Gesture Recognition, pages 336--341, 1998.
Recognition of Humans and Their Activities Using Video - Chellappa, Roy-Chowdhury.. Self-citation (Chellappa) (Correct)
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W. Zhao, R. Chellappa, and A. Krishnaswamy. Discriminant analysis of principal components for face recognition. In Proc. IEEE Intl. Conf. on Automatic Face and Gesture Recognition, pages 336-- 341, 1998.
Multiple-Exemplar Discriminant Analysis for Face Recognition - Shaohua Kevin Zhou (2004) Self-citation (Chellappa) (Correct)
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W. Zhao, R. Chellappa, and A. Krishnaswamy. Discriminant analysis of principal components for face recognition. Proc. of Intl. Conf. on Automatic Face and Gesture Recognition, pages 14--16, 1998.
Subspace Linear Discriminant Analysis for Face Recognition - Zhao, Chellappa, Phillips (1999) (7 citations) Self-citation (Zhao Chellappa) (Correct)
....identity from a database of known individuals, whereas in verification problems, the system needs to confirm or reject the claimed identity of the input face. Many methods have been proposed for face recognition [2, 1] Basically they can be divided into holistic template matching based systems [6, 8, 7, 10, 34, 23, 9] and geometrical localfeature based schemes [25, 11] Even though both types of systems have been successfully applied to the task of face recognition, they do have certain advantages and disadvantages. Thus appropriate schemes should be chosen based on the specific requirements of a given task. ....
....first projecting the original face image into the eigen subspace (eigenface) and then applying appropriate classifiers. Different pattern classifiers have been used for face recognition, including the Nearest Neighbor rule [6] Bayesian [7] and LDA FLD (Fisher Linear Discriminant) classifiers [34, 23, 10], to name a few. Of those classifiers, LDA is an attractive choice for face recognition verification tasks. This is because of the following reasons: 1) Unlike PCA which codes information relevant to compression, LDA encodes discriminatory information; 2) LDA is a good classifier when the input ....
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W. Zhao, R. Chellappa, and A. Krishnaswamy, "Discriminant Analysis of Principal Components for Face Recognition," in Proc. Third International Conference on Automatic Face and Gesture Recognition, pp. 336-341, Nara, Japan, 1998.
Subspace Linear Discriminant Analysis for Face Recognition - Zhao, Chellappa, Phillips (1999) (7 citations) Self-citation (Zhao Chellappa) (Correct)
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W. Zhao, A. Krishnaswamy, R. Chellappa, D.L. Swets, and J. Weng, "Discriminant Analysis of Principal Components for Face Recognition," Face Recognition: From Theory to Applications, Eds. H. Wechsler, P.J. Phillips, V. Bruce, F.F. Soulie and T.S. Huang, Berlin: Springer-Verlag, pp. 73-85, 1998.
Feature Selection for Hand-Geometry based Person Authentication - Vandana Roy And (Correct)
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A. Krishnaswamy W. Zhao, R. Chellappa. Discriminant analysis of principal components for face recognition. Proc. of the 3rd IEEE International Conference on Face and Gesture Recognition(FG), pages 14-- 16, April 1998.
Linear Projection Methods in Face Recognition under.. - Li, Ye, Kambhamettu (2004) (Correct)
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W. Zhao, A. Krishnaswamy, R. Chellappa, D. Swets, and J. Weng. Discriminant analysis of principal components for face recognition. In Wechsler, Phillips, Bruce, FogelmanSoulie, and Huang, editors, Face Recognition: From Theory to Applications, pages 73--85 Springer-- Verlag, 1998.
Fusion Of Face Recognition Algorithms For Video-Based.. - Marcialis, Roli (2003) (1 citation) (Correct)
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W. Zhao, A. Krishnaswamy, R. Chellappa, D. Swets, and J. Weng, Discriminant Analysis of Principal Components for Face Recognition, in Face Recognition: From Theory to Applications, Eds. H. Wechsler, P.J. Phillips, V. Bruce, F.F. Soulie and T.S. Huang, Springer-Verlag, pp. 73-85, 1998.
Originality and Contribution - In This Work (Correct)
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W. Zhao, A. Krishnaswamy, R. Chellappa, D. Swets, and J. Weng, Discriminant Analysis of Principal Components for Face Recognition, in Face Recognition: From Theory to Applications, H. Wechsler, P.J. Phillips, V. Bruce, F.F. Soulie and T.S. Huang Eds., Springer-Verlag, pp. 7385, 1998.
Comparing The Performance of the Discriminant Analysis.. - Feitosa, Thomaz, Veiga (1999) (Correct)
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W. Zhao, R. Chellappa and A. Krishnaswamy, "Discriminant Analysis of Principal Components for Face Recognition," Proc. 2nd International Conference on Automatic Face and Gesture Recognition, pp. 336-341, Japan, April, 1998.
A Maximum Uncertainty LDA-based approach for - Limited Sample Size (2004) (Correct)
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W. Zhao, R. Chellappa and A. Krishnaswamy, "Discriminant Analysis of Principal Components for Face Recognition", in Proc. 2 nd International Conference on Automatic Face and Gesture Recognition, 336-341, 1998.
Combination of Fisher Scores and Appearance Based - Features For Face (2003) (Correct)
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W. Zhao, R. Chellappa, and A. Krishnaswamy, "Discriminant analysis of principal components for face recognition," AFGR 1998.
Nonlinear Generative Models for Dynamic Shape - And Dynamic Appearance (Correct)
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W. Zhao, R. Chellappa, and A. Krishnaswamy, "Discriminant analysis of principal components for face recognition," in Proc. of 3rd International Conference on Automatic Face and Gesture Recognition, pp. 336--341, 1998.
Decision Fusion in Identity Verification using Facial Images - Czyz (2003) (Correct)
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W. Zhao, R. Chellappa, and A. Krishnaswamy. Discriminant analysis of principal components for face recognition. In Proc. of Int. Conf. on Automatic Face and Gesture Recognition, pages 336--341, 1998.
Face Recognition Across Pose and Illumination - Ralph Gross Simon (2004) (Correct)
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W. Zhao, A. Krishnaswamy, R. Chellappa, D. Swets, and J. Weng. Discriminant analysis of principal components for face recognition. In H. Wechsler, P. J. Phillips, V. Bruce, and T. Huang, editors, Face Recognition: From Theory to Applications. Springer Verlag, 1998.
Robust LDA Classification by Subsampling - Fidler, Leonardis (Correct)
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W. Zhao, A. Krishnaswamy, R. Chellappa, D. Swets, and J. Weng. "Discriminant Analysis of Principal Components for Face Recognition," Face Recognition: From Theory to Applications, Springer-Verlag, pp. 73-85, 1998.
Using Mixture Covariance Matrices to Improve Face and - Facial Expression Recognitions (Correct)
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W. Zhao, R. Chellappa and A. Krishnaswamy, "Discriminant Analysis of Principal Components for Face Recognition," Proc. 2nd International Conference on Automatic Face and Gesture Recognition, pp. 336-341, Japan, April, 1998.
Separate-Group Covariance Estimation With Insufficient Data .. - Object Recognition Carlos (2000) (Correct)
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W. Zhao, R. Chellappa and A. Krishnaswamy, "Discriminant Analysis of Principal Components for Face Recognition," Proc. 2nd International Conference on Automatic Face and Gesture Recognition, pp. 336-341, Japan, April, 1998.
Discriminant Analysis of Stochastic Models and Its - Application To Face (2003) (Correct)
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W. Zhao, R. Chellappa, and A. Krishnaswamy, "Discriminant analysis of principal components for face recognition," AFGR 1998.
On matching scores for LDA-based face - Verification Kittler Li (Correct)
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W Zhao et al, "Discriminant Analysis of Principal Components for Face Recognition, " In Harry Wechsler et al,editors,Face Recognition From theory to Applications, NATO ASI Series, pp. 73-85, Springer, 1998.
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