A novel Fisher criterion based St-subspace linear discriminant method for face recognition

Wensheng Chen; Pong Chi YUEN; Jian Huang; Jianhuang Lai

doi:10.1007/11596448_139

A novel Fisher criterion based S_t-subspace linear discriminant method for face recognition

Wensheng Chen^*, Pong Chi YUEN, Jian Huang, Jianhuang Lai

^*Corresponding author for this work

Department of Computer Science

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

3 Citations (Scopus)

Abstract

In this paper, a novel Fisher criterion is introduced and shown to be equivalent to the traditional Fisher criterion. Based on this new Fisher criterion and simultaneous diagonalization technique, a S_t-subspace Fisher discriminant (S_t-SFD) method is developed to deal with the small sample size (S3) problem in face recognition. The proposed method overcomes some drawbacks of existing LDA based algorithms. Also, our method has good computational complexity. Two public available databases, namely ORL and FERET databases, are exploited to evaluate the proposed algorithm. Comparing with existing LDA-based methods in solving the S3 problem, the proposed S _t-SFD method gives the best performance.

Original language	English
Title of host publication	Computational Intelligence and Security - International Conference, CIS 2005, Proceedings
Publisher	Springer Verlag
Pages	933-940
Number of pages	8
ISBN (Print)	3540308180, 9783540308188
DOIs	https://doi.org/10.1007/11596448_139
Publication status	Published - 2005
Event	International Conference on Computational Intelligence and Security, CIS 2005 - Xi'an, China Duration: 15 Dec 2005 → 19 Dec 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3801 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Computational Intelligence and Security, CIS 2005
Country/Territory	China
City	Xi'an
Period	15/12/05 → 19/12/05

Scopus Subject Areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/11596448_139

Cite this

Chen, W., YUEN, P. C., Huang, J., & Lai, J. (2005). A novel Fisher criterion based S_t-subspace linear discriminant method for face recognition. In Computational Intelligence and Security - International Conference, CIS 2005, Proceedings (pp. 933-940). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3801 LNAI). Springer Verlag. https://doi.org/10.1007/11596448_139

Chen, Wensheng ; YUEN, Pong Chi ; Huang, Jian et al. / A novel Fisher criterion based S_t-subspace linear discriminant method for face recognition. Computational Intelligence and Security - International Conference, CIS 2005, Proceedings. Springer Verlag, 2005. pp. 933-940 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A novel Fisher criterion based St-subspace linear discriminant method for face recognition",

abstract = "In this paper, a novel Fisher criterion is introduced and shown to be equivalent to the traditional Fisher criterion. Based on this new Fisher criterion and simultaneous diagonalization technique, a St-subspace Fisher discriminant (St-SFD) method is developed to deal with the small sample size (S3) problem in face recognition. The proposed method overcomes some drawbacks of existing LDA based algorithms. Also, our method has good computational complexity. Two public available databases, namely ORL and FERET databases, are exploited to evaluate the proposed algorithm. Comparing with existing LDA-based methods in solving the S3 problem, the proposed S t-SFD method gives the best performance.",

author = "Wensheng Chen and YUEN, {Pong Chi} and Jian Huang and Jianhuang Lai",

year = "2005",

doi = "10.1007/11596448_139",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Chen, W, YUEN, PC, Huang, J & Lai, J 2005, A novel Fisher criterion based S_t-subspace linear discriminant method for face recognition. in Computational Intelligence and Security - International Conference, CIS 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3801 LNAI, Springer Verlag, pp. 933-940, International Conference on Computational Intelligence and Security, CIS 2005, Xi'an, China, 15/12/05. https://doi.org/10.1007/11596448_139

A novel Fisher criterion based S_t-subspace linear discriminant method for face recognition. / Chen, Wensheng; YUEN, Pong Chi; Huang, Jian et al.
Computational Intelligence and Security - International Conference, CIS 2005, Proceedings. Springer Verlag, 2005. p. 933-940 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3801 LNAI).

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

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AB - In this paper, a novel Fisher criterion is introduced and shown to be equivalent to the traditional Fisher criterion. Based on this new Fisher criterion and simultaneous diagonalization technique, a St-subspace Fisher discriminant (St-SFD) method is developed to deal with the small sample size (S3) problem in face recognition. The proposed method overcomes some drawbacks of existing LDA based algorithms. Also, our method has good computational complexity. Two public available databases, namely ORL and FERET databases, are exploited to evaluate the proposed algorithm. Comparing with existing LDA-based methods in solving the S3 problem, the proposed S t-SFD method gives the best performance.

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Chen W, YUEN PC, Huang J, Lai J. A novel Fisher criterion based S_t-subspace linear discriminant method for face recognition. In Computational Intelligence and Security - International Conference, CIS 2005, Proceedings. Springer Verlag. 2005. p. 933-940. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11596448_139