An Optimal Subspace Analysis for Face Recognition

Haitao Zhao; Pong Chi Yuen; Jingyu Yang

doi:10.1007/978-3-540-25948-0_14

An Optimal Subspace Analysis for Face Recognition

Haitao Zhao^*, Pong Chi Yuen, Jingyu Yang

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

Fisher Linear Discriminant Analysis (LDA) has recently been successfully used as a data discriminantion technique. However, LDA-based face recognition algorithms suffer from a small sample size (S3) problem. It results in the singularity of the within-class scatter matrix S_w. To overcome this limitation, this paper has developed a novel subspace approach in determining the optimal projection. This algorithm effectively solves the small sample size problem and eliminates the possibility of losing discriminative information.

Original language	English
Title of host publication	Biometric Authentication
Subtitle of host publication	First International Conference, ICBA 2004, Hong Kong, China, July 15-17, 2004. Proceedings
Editors	David Zhang, Anil K. Jain
Publisher	Springer Berlin Heidelberg
Pages	95-101
Number of pages	7
Edition	1st
ISBN (Electronic)	9783540259480
ISBN (Print)	9783540221463
DOIs	https://doi.org/10.1007/978-3-540-25948-0_14
Publication status	Published - 2004
Event	1st International Conference on Biometric Authentication - , Hong Kong Duration: 15 Jul 2004 → 17 Jul 2004

Publication series

Name	Lecture Notes in Computer Science
Volume	3072
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Conference on Biometric Authentication
Abbreviated title	ICBA 2004
Country/Territory	Hong Kong
Period	15/07/04 → 17/07/04

Scopus Subject Areas

Theoretical Computer Science
Computer Science(all)

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10.1007/978-3-540-25948-0_14

Cite this

Zhao, H., Yuen, P. C., & Yang, J. (2004). An Optimal Subspace Analysis for Face Recognition. In D. Zhang, & A. K. Jain (Eds.), Biometric Authentication: First International Conference, ICBA 2004, Hong Kong, China, July 15-17, 2004. Proceedings (1st ed., pp. 95-101). (Lecture Notes in Computer Science; Vol. 3072). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-25948-0_14

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title = "An Optimal Subspace Analysis for Face Recognition",

abstract = "Fisher Linear Discriminant Analysis (LDA) has recently been successfully used as a data discriminantion technique. However, LDA-based face recognition algorithms suffer from a small sample size (S3) problem. It results in the singularity of the within-class scatter matrix Sw. To overcome this limitation, this paper has developed a novel subspace approach in determining the optimal projection. This algorithm effectively solves the small sample size problem and eliminates the possibility of losing discriminative information.",

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year = "2004",

doi = "10.1007/978-3-540-25948-0_14",

language = "English",

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Zhao, H, Yuen, PC & Yang, J 2004, An Optimal Subspace Analysis for Face Recognition. in D Zhang & AK Jain (eds), Biometric Authentication: First International Conference, ICBA 2004, Hong Kong, China, July 15-17, 2004. Proceedings. 1st edn, Lecture Notes in Computer Science, vol. 3072, Springer Berlin Heidelberg, pp. 95-101, 1st International Conference on Biometric Authentication, Hong Kong, 15/07/04. https://doi.org/10.1007/978-3-540-25948-0_14

An Optimal Subspace Analysis for Face Recognition. / Zhao, Haitao; Yuen, Pong Chi; Yang, Jingyu.
Biometric Authentication: First International Conference, ICBA 2004, Hong Kong, China, July 15-17, 2004. Proceedings. ed. / David Zhang; Anil K. Jain. 1st. ed. Springer Berlin Heidelberg, 2004. p. 95-101 (Lecture Notes in Computer Science; Vol. 3072).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - Yuen, Pong Chi

AU - Yang, Jingyu

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N2 - Fisher Linear Discriminant Analysis (LDA) has recently been successfully used as a data discriminantion technique. However, LDA-based face recognition algorithms suffer from a small sample size (S3) problem. It results in the singularity of the within-class scatter matrix Sw. To overcome this limitation, this paper has developed a novel subspace approach in determining the optimal projection. This algorithm effectively solves the small sample size problem and eliminates the possibility of losing discriminative information.

AB - Fisher Linear Discriminant Analysis (LDA) has recently been successfully used as a data discriminantion technique. However, LDA-based face recognition algorithms suffer from a small sample size (S3) problem. It results in the singularity of the within-class scatter matrix Sw. To overcome this limitation, this paper has developed a novel subspace approach in determining the optimal projection. This algorithm effectively solves the small sample size problem and eliminates the possibility of losing discriminative information.

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An Optimal Subspace Analysis for Face Recognition

Abstract

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