Subspace KDA algorithm for non-linear feature extraction in face identification

Wen Sheng Chen; Pong Chi YUEN; Jian Huang Lai

doi:10.1007/978-3-540-74377-4_116

Subspace KDA algorithm for non-linear feature extraction in face identification

Wen Sheng Chen^*, Pong Chi YUEN, Jian Huang Lai

^*Corresponding author for this work

Department of Computer Science

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

Abstract

Kernel discriminant analysis (KDA) method is a promising approach for non-linear feature extraction in face identification tasks. However, as a linear algorithm to address nonlinear problem, Fisher discriminant analysis (FDA) approach will not give a satisfactory performance. Moreover, FDA usually suffers from small sample size (S3) problem. To overcome these two shortcomings in FDA method, Shannon wavelet kernel based subspace FDA (SKDA) algorithm is developed in this paper. Two public databases such as FERET and CMU PIE databases are selected for evaluation. Comparing with the existing kernel based FDA-based methods, the proposed method gives superior results.

Original language	English
Title of host publication	Computational Intelligence and Security - International Conference, CIS 2006, Revised Selected Papers
Publisher	Springer Verlag
Pages	1106-1114
Number of pages	9
ISBN (Print)	9783540743767
DOIs	https://doi.org/10.1007/978-3-540-74377-4_116
Publication status	Published - 2007
Event	International Conference on Computational Intelligence and Security, CIS 2006 - Guangzhou, China Duration: 3 Nov 2006 → 6 Nov 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4456 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Computational Intelligence and Security, CIS 2006
Country/Territory	China
City	Guangzhou
Period	3/11/06 → 6/11/06

Scopus Subject Areas

Theoretical Computer Science
Computer Science(all)

User-Defined Keywords

Face identification
Kernel discriminant analysis
Shannon wavelet

Access to Document

10.1007/978-3-540-74377-4_116

Cite this

Chen, W. S., YUEN, P. C., & Lai, J. H. (2007). Subspace KDA algorithm for non-linear feature extraction in face identification. In Computational Intelligence and Security - International Conference, CIS 2006, Revised Selected Papers (pp. 1106-1114). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4456 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-74377-4_116

Chen, Wen Sheng ; YUEN, Pong Chi ; Lai, Jian Huang. / Subspace KDA algorithm for non-linear feature extraction in face identification. Computational Intelligence and Security - International Conference, CIS 2006, Revised Selected Papers. Springer Verlag, 2007. pp. 1106-1114 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Subspace KDA algorithm for non-linear feature extraction in face identification",

abstract = "Kernel discriminant analysis (KDA) method is a promising approach for non-linear feature extraction in face identification tasks. However, as a linear algorithm to address nonlinear problem, Fisher discriminant analysis (FDA) approach will not give a satisfactory performance. Moreover, FDA usually suffers from small sample size (S3) problem. To overcome these two shortcomings in FDA method, Shannon wavelet kernel based subspace FDA (SKDA) algorithm is developed in this paper. Two public databases such as FERET and CMU PIE databases are selected for evaluation. Comparing with the existing kernel based FDA-based methods, the proposed method gives superior results.",

keywords = "Face identification, Kernel discriminant analysis, Shannon wavelet",

author = "Chen, {Wen Sheng} and YUEN, {Pong Chi} and Lai, {Jian Huang}",

year = "2007",

doi = "10.1007/978-3-540-74377-4_116",

language = "English",

isbn = "9783540743767",

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

publisher = "Springer Verlag",

pages = "1106--1114",

booktitle = "Computational Intelligence and Security - International Conference, CIS 2006, Revised Selected Papers",

address = "Germany",

}

Chen, WS, YUEN, PC & Lai, JH 2007, Subspace KDA algorithm for non-linear feature extraction in face identification. in Computational Intelligence and Security - International Conference, CIS 2006, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4456 LNAI, Springer Verlag, pp. 1106-1114, International Conference on Computational Intelligence and Security, CIS 2006, Guangzhou, China, 3/11/06. https://doi.org/10.1007/978-3-540-74377-4_116

Subspace KDA algorithm for non-linear feature extraction in face identification. / Chen, Wen Sheng; YUEN, Pong Chi; Lai, Jian Huang.

Computational Intelligence and Security - International Conference, CIS 2006, Revised Selected Papers. Springer Verlag, 2007. p. 1106-1114 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4456 LNAI).

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

TY - GEN

T1 - Subspace KDA algorithm for non-linear feature extraction in face identification

AU - Chen, Wen Sheng

AU - YUEN, Pong Chi

AU - Lai, Jian Huang

PY - 2007

Y1 - 2007

N2 - Kernel discriminant analysis (KDA) method is a promising approach for non-linear feature extraction in face identification tasks. However, as a linear algorithm to address nonlinear problem, Fisher discriminant analysis (FDA) approach will not give a satisfactory performance. Moreover, FDA usually suffers from small sample size (S3) problem. To overcome these two shortcomings in FDA method, Shannon wavelet kernel based subspace FDA (SKDA) algorithm is developed in this paper. Two public databases such as FERET and CMU PIE databases are selected for evaluation. Comparing with the existing kernel based FDA-based methods, the proposed method gives superior results.

AB - Kernel discriminant analysis (KDA) method is a promising approach for non-linear feature extraction in face identification tasks. However, as a linear algorithm to address nonlinear problem, Fisher discriminant analysis (FDA) approach will not give a satisfactory performance. Moreover, FDA usually suffers from small sample size (S3) problem. To overcome these two shortcomings in FDA method, Shannon wavelet kernel based subspace FDA (SKDA) algorithm is developed in this paper. Two public databases such as FERET and CMU PIE databases are selected for evaluation. Comparing with the existing kernel based FDA-based methods, the proposed method gives superior results.

KW - Face identification

KW - Kernel discriminant analysis

KW - Shannon wavelet

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U2 - 10.1007/978-3-540-74377-4_116

DO - 10.1007/978-3-540-74377-4_116

M3 - Conference proceeding

AN - SCOPUS:38349027390

SN - 9783540743767

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1106

EP - 1114

BT - Computational Intelligence and Security - International Conference, CIS 2006, Revised Selected Papers

PB - Springer Verlag

T2 - International Conference on Computational Intelligence and Security, CIS 2006

Y2 - 3 November 2006 through 6 November 2006

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Chen WS, YUEN PC, Lai JH. Subspace KDA algorithm for non-linear feature extraction in face identification. In Computational Intelligence and Security - International Conference, CIS 2006, Revised Selected Papers. Springer Verlag. 2007. p. 1106-1114. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-74377-4_116

Subspace KDA algorithm for non-linear feature extraction in face identification

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