Random Subspace Two-Dimensional PCA for face recognition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4810 LNCS, Issue , 2007, Pages 655-664

  Nguyen, Nam ^a   Liu, Wanquan ^a   Venkatesh, Svetha ^a  

^a CURTIN UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; DATABASE SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; PROBLEM SOLVING;

RANDOM SUBSPACE (RS);

FACE RECOGNITION;

EID: 38349057308     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-77255-2_81     Document Type: Conference Paper

References (16)

1. Kirby, M., Sirovich, L.: Application of the KL procedure for the characterization of human faces. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(1), 103-108 (1990)
2. Sirovich, L., Kirby, M.: Low-dimensional procedure for characterization of human faces. Journal of the Optical Society of America 4, 519-524 (1987)
3. Turk, M., Pentland, A.: Eigenfaces for recognition. Cognitive Neuroscience 3(1), 71-86 (1991)
4. Kramer, M.A.: Nonlinear principle component analysis using auto-associative Neural networks. American Institution Chemical Engineering Journal 32(2) (1991)
5. Yunen, P., Lai, J.: Face representation using independent component analysis. Pattern recognition 35, 1247-1257 (2002)
6. Yang, M., Ahuja, N., Kriegman, D.: Face recognition using kernel Eigenfaces. In: Proceedings of International Conference of Image Processing, vol. 1, pp. 37-40 (2000)
7. Yang, J., Zhang, D., Frangi, A.F., Yang, J.: Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 131-137 (2004)
8. Kong, H., Wang, L., Teoh, E.K., Li, X., Wang, J., Venkateswarlu, R.: Generalized 2D principal component analysis for face image representation and recognition. Neural Networks 18, 585-594 (2005)
9. Visani, M., Garcia, C., Laurent, C.: Comparing robustness of two-dimensional PCA and Eigenfaces for face recognition. In: Campilho, A., Kamel, M. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 717-724. Springer, Heidelberg (2004)
10. Wang, L., Wang, X., Zhang, X., Feng, J.: The equivalence of two-dimensional PCA to line-based PCA. Pattern Recognition Letters 26(1), 57-60 (2005)
11. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 10(8), 832-844 (1998)
12. Wang, X., Tang, X.: Random sampling for subspace face recognition. International Journal of Computer Vision 70(1), 91-104 (2006)
13. Belhumeur, P., Hespanda, J., Kiregeman, D.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711-720 (1997)
14. Chen, L., Liao, H., Ko, M., Liin, J., Yu, G.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern recognition 33(10), 1713-1726 (2000)
15. Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intelligence 23(6), 643-660 (2001)
16. Lee, K., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intelligence 27(5), 684-698 (2005)