TY - GEN
T1 - A Novel One-Parameter Regularized Kernel Fisher Discriminant Method for Face Recognition
AU - Chen, Wensheng
AU - Yuen, Pong Chi
AU - Huang, Jian
AU - Dai, Daoqing
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2005/5/13
Y1 - 2005/5/13
N2 - Kernel-based regularization discriminant analysis (KRDA) is one of the promising approaches for solving small sample size problem in face recognition. This paper addresses the problem in regularization parameter reduction in KRDA. From computational complexity point of view, our goal is to develop a KRDA algorithm with minimum number of parameters, in which regularization process can be fully controlled. Along this line, we have developed a Kernel 1-parameter RDA (K1PRDA) algorithm (W. S. Chen, P C Yuen, J Huang and D. Q. Dai, "Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition," IEEE Transactions on SMC-B, to appear, 2005.). K1PRDA was developed based on a three-parameter regularization formula. In this paper, we propose another approach to formulate the one-parameter KRDA (1PRKFD) based on a two-parameter formula. Yale B database, with pose and illumination variations, is used to compare the performance of 1PRKFD algorithm, K1PRDA algorithm and other LDA-based algorithms. Experimental results show that both 1PRKFD and K1PRDA algorithms outperform the other LDA-based face recognition algorithms. The performance between 1PRKFD and K1PRDA algorithms are comparable. This concludes that our methodology in deriving the one-parameter KRDA is stable.
AB - Kernel-based regularization discriminant analysis (KRDA) is one of the promising approaches for solving small sample size problem in face recognition. This paper addresses the problem in regularization parameter reduction in KRDA. From computational complexity point of view, our goal is to develop a KRDA algorithm with minimum number of parameters, in which regularization process can be fully controlled. Along this line, we have developed a Kernel 1-parameter RDA (K1PRDA) algorithm (W. S. Chen, P C Yuen, J Huang and D. Q. Dai, "Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition," IEEE Transactions on SMC-B, to appear, 2005.). K1PRDA was developed based on a three-parameter regularization formula. In this paper, we propose another approach to formulate the one-parameter KRDA (1PRKFD) based on a two-parameter formula. Yale B database, with pose and illumination variations, is used to compare the performance of 1PRKFD algorithm, K1PRDA algorithm and other LDA-based algorithms. Experimental results show that both 1PRKFD and K1PRDA algorithms outperform the other LDA-based face recognition algorithms. The performance between 1PRKFD and K1PRDA algorithms are comparable. This concludes that our methodology in deriving the one-parameter KRDA is stable.
KW - Face Recognition
KW - Conjugate Gradient Method
KW - Illumination Variation
KW - Rank1 Accuracy
KW - Small Sample Size Problem
UR - http://www.scopus.com/inward/record.url?scp=25144431786&partnerID=8YFLogxK
U2 - 10.1007/11492542_9
DO - 10.1007/11492542_9
M3 - Conference proceeding
AN - SCOPUS:25144431786
SN - 9783540261544
T3 - Lecture Notes in Computer Science
SP - 67
EP - 74
BT - Pattern Recognition and Image Analysis
A2 - Marques, Jorge S.
A2 - de la Blanca, Nicolás Pérez
A2 - Pina, Pedro
PB - Springer
CY - Berlin, Heidelberg
T2 - Second Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2005
Y2 - 7 June 2005 through 9 June 2005
ER -