Shannon wavelet kernel based subspace LDA approach in face recognition

Wen Sheng Chen; Bin Fang; Pong Chi Yuen; Jian Huang Lai

doi:10.1109/ICCIAS.2006.294226

Shannon wavelet kernel based subspace LDA approach in face recognition

Wen Sheng Chen^*, Bin Fang, 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

3 Citations (Scopus)

Abstract

It is well-known that the distribution of face images with different pose, illumination and face expression is complex and nonlinear. The traditional linear methods, such as linear discriminant analysis (LDA), will not give a satisfactory performance. In addition, LDA always suffers from small sample size (S3) problem, which always occurs when the sample size is smaller than the dimensionality of feature vector. To overcome these limitations, Shannon wavelet kernel combining with subspace LDA (SWKSLDA) algorithm is developed. Two databases, namely FERET and CMU PIE databases, are selected for evaluation. Comparing with the existing LDA-based methods, the proposed method gives superior results.

Original language	English
Title of host publication	2006 International Conference on Computational Intelligence and Security, CIS 2006
Publisher	IEEE Computer Society
Pages	708-713
Number of pages	6
ISBN (Electronic)	1424406056
ISBN (Print)	1424406048, 9781424406050
DOIs	https://doi.org/10.1109/ICCIAS.2006.294226
Publication status	Published - 3 Nov 2006
Event	2006 International Conference on Computational Intelligence and Security, CIS 2006 - Guangzhou, China Duration: 3 Nov 2006 → 6 Nov 2006 https://ieeexplore.ieee.org/xpl/conhome/4072023/proceeding https://link.springer.com/book/10.1007/978-3-540-74377-4

Publication series

Name	International Conference on Computational Intelligence and Security, CIS

Conference

Conference	2006 International Conference on Computational Intelligence and Security, CIS 2006
Country/Territory	China
City	Guangzhou
Period	3/11/06 → 6/11/06
Internet address	https://ieeexplore.ieee.org/xpl/conhome/4072023/proceeding https://link.springer.com/book/10.1007/978-3-540-74377-4

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10.1109/ICCIAS.2006.294226

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Chen, W. S., Fang, B., Yuen, P. C., & Lai, J. H. (2006). Shannon wavelet kernel based subspace LDA approach in face recognition. In 2006 International Conference on Computational Intelligence and Security, CIS 2006 (pp. 708-713). (International Conference on Computational Intelligence and Security, CIS). IEEE Computer Society. https://doi.org/10.1109/ICCIAS.2006.294226

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title = "Shannon wavelet kernel based subspace LDA approach in face recognition",

abstract = "It is well-known that the distribution of face images with different pose, illumination and face expression is complex and nonlinear. The traditional linear methods, such as linear discriminant analysis (LDA), will not give a satisfactory performance. In addition, LDA always suffers from small sample size (S3) problem, which always occurs when the sample size is smaller than the dimensionality of feature vector. To overcome these limitations, Shannon wavelet kernel combining with subspace LDA (SWKSLDA) algorithm is developed. Two databases, namely FERET and CMU PIE databases, are selected for evaluation. Comparing with the existing LDA-based methods, the proposed method gives superior results.",

author = "Chen, {Wen Sheng} and Bin Fang and Yuen, {Pong Chi} and Lai, {Jian Huang}",

note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2006 International Conference on Computational Intelligence and Security, CIS 2006 ; Conference date: 03-11-2006 Through 06-11-2006",

year = "2006",

month = nov,

day = "3",

doi = "10.1109/ICCIAS.2006.294226",

language = "English",

isbn = "1424406048",

series = "International Conference on Computational Intelligence and Security, CIS",

publisher = "IEEE Computer Society",

pages = "708--713",

booktitle = "2006 International Conference on Computational Intelligence and Security, CIS 2006",

address = "United States",

url = "https://ieeexplore.ieee.org/xpl/conhome/4072023/proceeding, https://link.springer.com/book/10.1007/978-3-540-74377-4",

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Chen, WS, Fang, B, Yuen, PC & Lai, JH 2006, Shannon wavelet kernel based subspace LDA approach in face recognition. in 2006 International Conference on Computational Intelligence and Security, CIS 2006. International Conference on Computational Intelligence and Security, CIS, IEEE Computer Society, pp. 708-713, 2006 International Conference on Computational Intelligence and Security, CIS 2006, Guangzhou, China, 3/11/06. https://doi.org/10.1109/ICCIAS.2006.294226

Shannon wavelet kernel based subspace LDA approach in face recognition. / Chen, Wen Sheng; Fang, Bin; Yuen, Pong Chi et al.
2006 International Conference on Computational Intelligence and Security, CIS 2006. IEEE Computer Society, 2006. p. 708-713 (International Conference on Computational Intelligence and Security, CIS).

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

TY - GEN

T1 - Shannon wavelet kernel based subspace LDA approach in face recognition

AU - Chen, Wen Sheng

AU - Fang, Bin

AU - Yuen, Pong Chi

AU - Lai, Jian Huang

PY - 2006/11/3

Y1 - 2006/11/3

N2 - It is well-known that the distribution of face images with different pose, illumination and face expression is complex and nonlinear. The traditional linear methods, such as linear discriminant analysis (LDA), will not give a satisfactory performance. In addition, LDA always suffers from small sample size (S3) problem, which always occurs when the sample size is smaller than the dimensionality of feature vector. To overcome these limitations, Shannon wavelet kernel combining with subspace LDA (SWKSLDA) algorithm is developed. Two databases, namely FERET and CMU PIE databases, are selected for evaluation. Comparing with the existing LDA-based methods, the proposed method gives superior results.

AB - It is well-known that the distribution of face images with different pose, illumination and face expression is complex and nonlinear. The traditional linear methods, such as linear discriminant analysis (LDA), will not give a satisfactory performance. In addition, LDA always suffers from small sample size (S3) problem, which always occurs when the sample size is smaller than the dimensionality of feature vector. To overcome these limitations, Shannon wavelet kernel combining with subspace LDA (SWKSLDA) algorithm is developed. Two databases, namely FERET and CMU PIE databases, are selected for evaluation. Comparing with the existing LDA-based methods, the proposed method gives superior results.

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U2 - 10.1109/ICCIAS.2006.294226

DO - 10.1109/ICCIAS.2006.294226

M3 - Conference proceeding

AN - SCOPUS:38549132276

SN - 1424406048

SN - 9781424406050

T3 - International Conference on Computational Intelligence and Security, CIS

SP - 708

EP - 713

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

PB - IEEE Computer Society

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

Y2 - 3 November 2006 through 6 November 2006

ER -

Shannon wavelet kernel based subspace LDA approach in face recognition

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