Image covariance-based subspace method for face recognition

Pattern Recognition

Volumn 40, Issue 5, 2007, Pages 1592-1604

  Kim, Chunghoon ^a   Choi, Chong Ho ^a  

^a Seoul National University   (South Korea)

Author keywords

Face recognition; Image covariance matrix; Linear discriminant analysis; Principal component analysis; Subspace method; Windowed feature

Indexed keywords

DATABASE SYSTEMS; IMAGE ANALYSIS; MATRIX ALGEBRA; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL MECHANICS;

IMAGE COVARIANCE; LINEAR DISCRIMINANT ANALYSIS; PIXELS; SUBSPACE METHODS;

FACE RECOGNITION;

EID: 33846365956     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2006.09.010     Document Type: Article

References (34)

1. Turk M., and Pentland A. Eigenfaces for recognition. J. Cognitive Neurosci. 3 1 (1991) 71-86
2. Belhumeur P.N., Hespanha J.P., and Kriegman D.J. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19 7 (1997) 711-720
3. Pentland A., Moghaddam B., and Starner T. View-based and modular eigenspaces for face recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1994) 84-91
4. Martinez A., and Kak A.C. PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23 2 (2001) 228-233
5. Yang J., Zhang D., Frangi A.F., and Yang J.-y. Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26 1 (2004) 131-137
6. Swets D.L., and Weng J. Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 18 8 (1996) 831-836
7. Liu C., and Wechsler H. Robust coding schemes for indexing and retrieval from large face databases. IEEE Trans. Image Process. 9 1 (2000) 132-137
8. Mahamud S., and Hebert M. Minimum risk distance measure for object recognition. Proceedings of the Ninth IEEE International Conference on Computer Vision (2003) 242-248
9. Wang X., and Tang X. A unified framework for subspace face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26 9 (2004) 1222-1228
10. Yu H., and Yang J. A direct LDA algorithms for high-dimensional data with application to face recognition. Pattern Recognition 34 (2001) 2067-2070
11. Lu J., Plataniotis K.N., and Venetsanopoulos A.N. Face recognition using LDA-based algorithms. IEEE Trans. Neural Networks 14 1 (2003) 195-200
12. Chen L.-F., Liao H.-Y.M., Ko M.-T., Lin J.-C., and Yu G.-J. A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition 33 (2000) 1713-1726
13. Cevikalp H., Neamtu M., Wilkes M., and Barkana A. Discriminative common vectors for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 27 1 (2005) 4-13
14. Fukunaga K. Introduction to Statistical Pattern Recognition. second ed. (1990), Academic Press, New York
15. Wang X., and Tang X. Random sampling LDA for face recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition vol. 2 (2004) 259-265
16. Moghaddam B., Jebara T., and Pentland A. Bayesian face recognition. Pattern Recognition 33 (2000) 1771-1782
17. Moghaddam B. Principal manifolds and probabilistic subspaces for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 24 6 (2002) 780-788
18. Jain A.K. Fundamentals of Digital Image Processing (1989), Prentice-Hall, Englewood Cliffs, NJ
19. Yang J., and Yang J.-y. From image vector to matrix: a straightforward image projection technique-IMPCA vs. PCA. Pattern Recognition 35 (2002) 1997-1999
20. Yang J., Zhang D., Yong X., and Yang J.-y. Two-dimensional discriminant transform for face recognition. Pattern Recognition 38 (2005) 1125-1129
21. Xiong H., Swamy M.N.S., and Ahmad M.O. Two-dimensional FLD for face recognition. Pattern Recognition 38 (2005) 1121-1124
22. Phillips P.J., Moon H., Rizvi S.A., and Rauss P.J. The FERET evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22 10 (2000) 1090-1104
23. Sim T., Baker S., and Bsat M. The CMU pose. Proceedings of the Fifth International Conference on Automatic Face and Gesture Recognition (2002) 46-51
24. AT&T Laboratories Cambridge, The ORL database of faces, 〈http://www.uk.research.att.com/facedatabase.html〉.
25. Webb A. Statistical Pattern Recognition. second ed. (2002), Wiley, West Sussex
26. Moghaddam B., and Pentland A. Probabilistic visual learning for object representation. IEEE Trans. Pattern Anal. Mach. Intell. 19 7 (1997) 696-710
27. Liu C., and Wechsler H. Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 11 4 (2002) 467-476
28. Price J.R., and Gee T.F. Face recognition using direct, weighted linear discriminant analysis and modular subspaces. Pattern Recognition 38 (2005) 209-219
29. Kim C., Oh J., and Choi C.-H. Combined subspace method using global and local features for face recognition. Proceedings of the International Joint Conference on Neural Networks (2005) 2030-2035
30. Parzen E. On estimation of a probability density function and mode. Ann. Math. Statist. 33 (1962) 1065-1076
31. Kwak N., and Choi C.-H. Input feature selection by mutual information based on Parzen window. IEEE Trans. Pattern Anal. Mach. Intell. 24 12 (2002) 1667-1671
32. Kwak N., and Choi C.-H. Feature extraction using mutual information based on Parzen window. Proceedings of the International Conference on Artificial Neural Networks (2003) 278-281
33. National Institute of Standards and Technology, The Color FERET database, 〈http://www.nist.gov/humanid/colorferet〉.
34. W. Zhao, R. Chellappa, P.J. Phillips, Subspace linear discriminant analysis for face recognition, Technical Report CAR-TR-914, Center for Automation Research, University of Maryland, 1999.