TY - GEN
T1 - Face classification based on Shannon wavelet kernel and modified fisher criterion
AU - Chen, Wen Sheng
AU - Yuen, Pong Chi
AU - Huang, Jian
AU - Lai, Jianhuang
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2006
Y1 - 2006
N2 - This paper addresses nonlinear feature extraction and Small Sample Size (S3) problems in face recognition. In sample feature space, the distribution of face images is nonlinear because of complex variations in pose, illumination and face expression. The performance of classical linear method, such as Fisher discriminant analysis (FDA), will degrade. To overcome pose and illumination problems, Shannon wavelet kernel is constructed and utilized for non-linear feature extraction. Based on a modified Fisher criterion, simultaneous diagonalization technique is exploited to deal with S3 problem, which often occurs in FDA based methods. Shannon wavelet kernel based subspace Fisher discriminant (SWK-SFD) method is then developed in this paper. The proposed approach not only overcomes some drawbacks of existing FDA based algorithms, but also has good computational complexity. Two databases, namely FERET and CMU PIE face databases, are selected for evaluation. Comparing with the existing FDA-based methods, the proposed method gives superior results.
AB - This paper addresses nonlinear feature extraction and Small Sample Size (S3) problems in face recognition. In sample feature space, the distribution of face images is nonlinear because of complex variations in pose, illumination and face expression. The performance of classical linear method, such as Fisher discriminant analysis (FDA), will degrade. To overcome pose and illumination problems, Shannon wavelet kernel is constructed and utilized for non-linear feature extraction. Based on a modified Fisher criterion, simultaneous diagonalization technique is exploited to deal with S3 problem, which often occurs in FDA based methods. Shannon wavelet kernel based subspace Fisher discriminant (SWK-SFD) method is then developed in this paper. The proposed approach not only overcomes some drawbacks of existing FDA based algorithms, but also has good computational complexity. Two databases, namely FERET and CMU PIE face databases, are selected for evaluation. Comparing with the existing FDA-based methods, the proposed method gives superior results.
KW - Face classification
KW - Fisher discriminant analysis
KW - Kernel method
KW - Small sample size problem
KW - Wavelet
UR - http://www.scopus.com/inward/record.url?scp=33750836138&partnerID=8YFLogxK
U2 - 10.1109/FGR.2006.41
DO - 10.1109/FGR.2006.41
M3 - Conference proceeding
AN - SCOPUS:33750836138
SN - 0769525032
SN - 9780769525037
T3 - FGR 2006: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
SP - 467
EP - 472
BT - FGR 2006
T2 - FGR 2006: 7th International Conference on Automatic Face and Gesture Recognition
Y2 - 10 April 2006 through 12 April 2006
ER -