Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition

Pattern Recognition Letters

Volumn 26, Issue 2, 2005, Pages 181-191

  Lu, Juwei ^a   Plataniotis, K N ^a   Venetsanopoulos, A N ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

Face recognition; Linear discriminant analysis; Regularization; Small sample size

Indexed keywords

ALGEBRA; EIGENVALUES AND EIGENFUNCTIONS; HUMAN COMPUTER INTERACTION; IMAGE PROCESSING; LEARNING SYSTEMS; PATTERN RECOGNITION SYSTEMS; PROBLEM SOLVING; STATISTICAL METHODS;

LINEAR DISCRIMINANT ANALYSIS; REGULARIZATION; SMALL SAMPLE SIZE;

FACE RECOGNITION;

EID: 10044254164     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2004.09.014     Document Type: Article

References (29)

1. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J., 1997. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Machine Intell. 19 (7), 711-720.
2. Chellappa, R., Wilson, C., Sirohey, S., 1995. Human and machine recognition of faces: A survey. Proc. IEEE 83, 705-740.
3. Chen, L.-F., Liao, H.-Y.M., Ko, M.-T., Lin, J.-C., Yu, O.-J., 2000. A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition 33, 1713-1726.
4. Fisher, R., 1936. The use of multiple measures in taxonomic problems. Ann. Eugenics 7, 179-188.
5. Friedman, J.H., 1989. Regularized discriminant analysis. J. Amer. Statist. Assoc. 84, 165-175.
6. Gong, S., McKenna, S.J., Psarrou, A., 2000. Dynamic Vision From Images to Face Recognition. Imperial College Press, World Scientific Publishing.
7. Hong, Z.-Q., Yang, J.-Y., 1991. Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognition 24 (4), 317-324.
8. Huang, J., Yuen, P.C., Chen, W.-S., Lai, J.H., 2003. Component-based LDA method for face recognition with one training sample. In: Proc. IEEE Internat. Workshop on Analysis and Modeling of Faces and Gestures, Nice, France.
9. Kittler, J., Hatef, M., Duin, R.P., Matas, J., 1998. On combining classifiers. IEEE Trans. Pattern Anal. Machine Intell. 20 (3), 226-239.
10. Li, S.Z., Lu, J., 1999. Face recognition using the nearest feature line method. IEEE Trans. Neural Networks 10, 439-443.
11. Liu, C., Wechsler, H., 2002. Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 11 (4), 467-476.
12. Liu, K., Cheng, Y., Yang, J., 1992a. A generalized optimal set of discriminant vectors. Pattern Recognition 25, 731-7739.
13. Liu, K., Cheng, Y., Yang, J., Liu, X., 1992b. An efficient algorithm for foley-sammon optimal set of discriminant vectors by algebraic method. Internat. J. Pattern Recognition Artif. Intell. 6, 817-829.
14. Liu, K., Cheng, Y., Yang, J., 1993. Algebraic feature extraction for image recognition based on an optimal discriminant criterion. Pattern Recognition 26, 903-911.
15. Lu, J., Plataniotis, K., Venetsanopoulos, A., 2003a. Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans. Neural Networks 14 (1), 117-126.
16. Lu, J., Plataniotis, K., Venetsanopoulos, A., 2003b. Face recognition using LDA based algorithms. IEEE Trans. Neural Networks 14 (1), 195-200.
17. Lu, J., Plataniotis, K., Venetsanopoulos, A., 2003c. Regularized discriminant analysis for the small sample size problem in face recognition. Pattern Recognition Lett. 24 (16), 3079-3087.
18. Phillips, P.J., Wechsler, H., Huang, J., Rauss, P., 1998. The FERET database and evaluation procedure for face récognition algorithms. Image Vision Comput. J. 16 (5), 295-306.
19. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J., 2000. The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Machine Intell. 22 (10), 1090-1104.
20. Raudys, S.J., Jain, A.K., 1991. Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans. Pattern Anal. Machine Intell. 13 (3), 252-264.
21. Samal, A., Iyengar, P.A., 1992. Automatic recognition and analysis of human faces and facial expressions: A survey. Pattern Recognition 25, 65-77.
22. Tian, Q., Barbero, M., Gu, Z., Lee, S., 1986. Image classification by the foley-sammon transform. Opt. Eng. 25 (7), 834-840.
23. Turk, M., 2001. A random walk through eigenspace. IEICE Trans. Inf. Systems E84-D (12), 1586-1695.
24. Turk, M.A., Pentland, A.P., 1991. Eigenfaces for recognition. J. Cognitive Neurosci. 3 (1), 71-86.
25. Valentin, D., Alice, H.A., Toole, J.O., Cottrell, G.W., 1994. Connectionist models of face processing: A survey. Pattern Recognition 27 (9), 1209-1230.
26. Ye, J., Li, Q., 2004. LDA/QR: An efficient and effective dimension reduction algorithm and its theoretical foundation. Pattern Recognition 37 (4), 851-854.
27. Yu, H., Yang, J., 2001. A direct LDA algorithm for high-dimensional data - with application to face recognition. Pattern Recognition 34, 2067-2070.
28. Zhao, W., Chellappa, R., Phillips, J., 1999. Subspace linear discriminant analysis for face recognition. Technical Report, CS-TR4009, University of Maryland.
29. Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A., 2003. Face recognition: A literature survey. ACM Comput. Surveys 35 (4), 399-458.