Browse > Article

Face Recognition using LDA Mixture Model  

Kim Hyun-Chul (포항공과대학교 컴퓨터공학과)
Kim Daijin (포항공과대학교 컴퓨터공학과)
Bang Sung-Yang (포항공과대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.32, no.8, 2005 , pp. 789-794 More about this Journal
Abstract
LDA (Linear Discriminant Analysis) provides the projection that discriminates the data well, and shows a very good performance for face recognition. However, since LDA provides only one transformation matrix over whole data, it is not sufficient to discriminate the complex data consisting of many classes like honan faces. To overcome this weakness, we propose a new face recognition method, called LDA mixture model, that the set of alf classes are partitioned into several clusters and we get a transformation matrix for each cluster. This detailed representation will improve the classification performance greatly. In the simulation of face recognition, LDA mixture model outperforms PCA, LDA, and PCA mixture model in terms of classification performance.
Keywords
LDA; LDA mixture model; PCA; PCA mixture model; face recognition;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Hyvarinen and E. Oja, 'A Fast Fixed Point Algorithms for Independent Component Analysis,' Neural Computation, Vol.9, No.7, pp.1483-1492, Oct., 1997   DOI   ScienceOn
2 M.S. Bartlett, J.R. Movellan and T.J. Sejnowski, 'Face recognition by independent component analysis,' IEEE Transactions on Neural Networks 13(6), 2002   DOI   ScienceOn
3 J. Fortuna and D. Capson, 'ICA Filters For Lighting Invariant Face Recognition,' Proceedings of the 17th International Conference on Pattern Recognition, 2004   DOI
4 R. Duda, P. Hart, D. Stork, Pattern Classification, Wiley, New York, 2001
5 M. Tipping, C. Bishop, 'Mixtures of probabilistic principal component analyzers,' Neural Computation, 11, 443-482, 1999   DOI   ScienceOn
6 P. Dempster, N. Laird, D. Rubin, 'Maximum likelihood from incomplete data via the EM algorithm,' Journal of the Royal Statistical Society: series-B, 39(4), 1-38, 1977
7 L. Wang and T. K. Tan, 'Experimental results of face description based on the 2nd-order eigenface method,' ISO/MPEG m6001, Geneva, May, 2000
8 M. Turk and A. Pentland, 'Eigenfaces for Recognition,' J. Cognitive Neuroscience, 3(1), 1991
9 Belhumeur P. N., Hespanha J. P., Kriegmaqn D. J., 'Eigenfaces vs. Fisherfaces : recognition using class specific Linear Projection,' IEEE Trans. on Pattern Analysis and Machine Intell., Vol.19, No.7, pp.711-720, 1997   DOI   ScienceOn
10 K. Etemad, R. Chellappa, 'Discriminant analysis for recognition of human face images,' Journal of the Optical Society of America A, 14(8), 1724-1733, 1997   DOI
11 R. Brunelli and T. Poggio, 'Face Recognition: Features versus Templates,' IEEE Trans. PAMI. , Vol. 15, pp.1042-1052, 1993   DOI   ScienceOn
12 R. Chellappa, C.Wilson, and S.Sirohey, 'Human and machine recognition of faces: A survey.'Proc. IEEE, Vo1. 83, pp. 705-740, May 1995   DOI   ScienceOn
13 W. Zhao, R. Chellappa, P.J. Phillips and A. Rosenfeld, 'Face Recognition: A Literature Survery,' UMD CfAR Technical Report CAR-TR-948, 2000