TY - JOUR
T1 - GA-Fisher
T2 - A new LDA-based face recognition algorithm with selection of principal components
AU - Zheng, Wei Shi
AU - Lai, Jian Huang
AU - Yuen, Pong C.
N1 - Funding Information:
Manuscript received May 6, 2004; revised November 26, 2004. This work was supported in part by the NSFC under Grant 60144001, the NSF of GuangDong, China under Grant 021766, the RGC Earmarked Research Grant HKBU-2119/03E, and the Key (Key grant) Project of Chinese Ministry of Education under Grant 105134. The Associate Editor recommending this paper was Vit-torro Marino.
PY - 2005/10
Y1 - 2005/10
N2 - This paper addresses the dimension reduction problem in Fisherface for face recognition. When the number of training samples is less than the image dimension (total number of pixels), the within-class scatter matrix (Sw) in Linear Discriminant Analysis (LDA) is singular, and Principal Component Analysis (PCA) is suggested to employ in Fisherface for dimension reduction of Sw so that it becomes nonsingular. The popular method is to select the largest nonzero eigenvalues and the corresponding eigenvectors for LDA. To attenuate the illumination effect, some researchers suggested removing the three eigenvectors with the largest eigenvalues and the performance is improved. However, as far as we know, there is no systematic way to determine which eigenvalues should be used. Along this line, this paper proposes a theorem to interpret why PCA can be used in LDA and an automatic and systematic method to select the eigenvectors to be used in LDA using a Genetic Algorithm (GA). A GA-PCA is then developed. It is found that some small eigenvectors should also be used as part of the basis for dimension reduction. Using the GA-PCA to reduce the dimension, a GA-Fisher method is designed and developed. Comparing with the traditional Fisherface method, the proposed GA-Fisher offers two additional advantages. First, optimal bases for dimensionality reduction are derived from GA-PCA. Second, the computational efficiency of LDA is improved by adding a whitening procedure after dimension reduction. The Face Recognition Technology (FERET) and Carnegie Mellon University Pose, Illumination, and Expression (CMU PIE) databases are used for evaluation. Experimental results show that almost 5% improvement compared with Fisherface can be obtained, and the results are encouraging.
AB - This paper addresses the dimension reduction problem in Fisherface for face recognition. When the number of training samples is less than the image dimension (total number of pixels), the within-class scatter matrix (Sw) in Linear Discriminant Analysis (LDA) is singular, and Principal Component Analysis (PCA) is suggested to employ in Fisherface for dimension reduction of Sw so that it becomes nonsingular. The popular method is to select the largest nonzero eigenvalues and the corresponding eigenvectors for LDA. To attenuate the illumination effect, some researchers suggested removing the three eigenvectors with the largest eigenvalues and the performance is improved. However, as far as we know, there is no systematic way to determine which eigenvalues should be used. Along this line, this paper proposes a theorem to interpret why PCA can be used in LDA and an automatic and systematic method to select the eigenvectors to be used in LDA using a Genetic Algorithm (GA). A GA-PCA is then developed. It is found that some small eigenvectors should also be used as part of the basis for dimension reduction. Using the GA-PCA to reduce the dimension, a GA-Fisher method is designed and developed. Comparing with the traditional Fisherface method, the proposed GA-Fisher offers two additional advantages. First, optimal bases for dimensionality reduction are derived from GA-PCA. Second, the computational efficiency of LDA is improved by adding a whitening procedure after dimension reduction. The Face Recognition Technology (FERET) and Carnegie Mellon University Pose, Illumination, and Expression (CMU PIE) databases are used for evaluation. Experimental results show that almost 5% improvement compared with Fisherface can be obtained, and the results are encouraging.
KW - Dimension reduction
KW - Face recognition
KW - GA-PCA
KW - Genetic algorithms
KW - LDA
KW - PCA
UR - http://www.scopus.com/inward/record.url?scp=26844439180&partnerID=8YFLogxK
U2 - 10.1109/TSMCB.2005.850175
DO - 10.1109/TSMCB.2005.850175
M3 - Journal article
C2 - 16240780
AN - SCOPUS:26844439180
SN - 1083-4419
VL - 35
SP - 1065
EP - 1078
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IS - 5
ER -