Incremental two-dimensional linear discriminant analysis with applications to face recognition

Journal of Network and Computer Applications

Volumn 33, Issue 3, 2010, Pages 314-322

  Wang, Jian Gang ^a   Sung, Eric ^b   Yau, Wei Yun ^a  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Batch 2DLDA; Bilateral 2DLDA; Face recognition; Incremental 2DLDA; Incremental LDA

Indexed keywords

BATCH 2DLDA; CLOSED FORM SOLUTIONS; DISCRIMINANT EIGENSPACE; FACIAL FEATURE; INCREMENTAL LEARNING; LINEAR DISCRIMINANT ANALYSIS; SCATTER MATRIX; SMALL SAMPLE SIZE PROBLEMS; SUBSPACE ANALYSIS; TRAINING DATA; WITHIN-CLASS SCATTER MATRIX;

DISCRIMINANT ANALYSIS; FEATURE EXTRACTION; TWO DIMENSIONAL;

FACE RECOGNITION;

EID: 77951205392     PISSN: 10848045     EISSN: 10958592     Source Type: Journal    
DOI: 10.1016/j.jnca.2009.12.014     Document Type: Article

References (30)

1. Belhumeur PN, Hespanha J, Kriegman DJ. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 1997;19(7):711-20.
2. Chandrasekaran S., Manjunath B.S., Wang Y.F., Winkler J., and Zhang H. An eigenspace update algorithm for image analysis. Graphical Models Image Process 59 5 (1997) 321-332
3. Chen L., Liao H., Ko M., Lin J., and Yu G. A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition 33 10 (2000) 1713-1726
4. DeGroat R.D., and Roberts R. Efficient, numerically stabilized rank-one eigenstructure updating. IEEE Trans Acoust Speech Signal Process 38 2 (1990) 301-316
5. Demir G.K., and Ozmehmet K. Online local learning algorithms for linear discriminant analysis. Pattern Recognition Lett 26 4 (2005) 421-431
6. Fritzsch K. A matrix approach to character recognition. Pattern Recognition 11 3 (1979) 165-168
7. Hall P, Martin R. Incremental eigen analysis for classification. In: Proceedings of the Br Mach Vision Conf 1998, p. 286-95
8. Hall P., Marshall D., and Martin R. Merging and splitting eigenspace models. IEEE Trans Pattern Anal Mach Intell 22 9 (2000) 1042-1049
9. Hiraoka K, Hidai K, Hamahira M, Mizoguchi H, Mishima T, Yoshizawa S. Successive learning of linear discriminant analysis: Sanger-type algorithm. In: Proceedings of the IEEE international conference on pattern recognition, 2000. p. 664-7.
10. Huang R, Liu QS, Lu HQ, Ma SD. Solving the small sample size problem of LDA. In: IEEE international conference on pattern recognition, 2002, p. 29-32
11. Kim TK, Wong S-F, Stenger B, Kittler J, Cipolla R. Incremental linear discriminant analysis using sufficient spanning set approximations. In: Proceedings of the IEEE international conference on computer vision and pattern recognition 2005.
12. Kong H, Wang L, Teoh EK, Wang J-G, Venkateswarlu R. A framework of 2D Fisher discriminant analysis: application to face recognition with small number of training samples. In: Proceedings of the IEEE international conference on computer vision and pattern recognition 2005. p. 1083-8.
13. Li M, Yuan B. A novel statically linear discriminant analysis for image matrix: two dimensional Fisherfaces. In: Proceedings of the seventh international conference on signal processing (ICSP04), 2004. Vol. 2, p. 1419-22.
14. Li X, Hu W, Zhang Z, Zhang X, Luo G. Robust visual tracking based on incremental tensor subspace learning. In: Proceedings of the IEEE international conference on computer vision (ICCV), 2007.
15. Li G, Liang D, Huang Q, Jiang S, Gao W. Object tracking using incremental 2D-LDA learning and Bayes inference. In: Proceedings of the IEEE international conference on image processing, San Diego, CA, USA, 2008. p. 1567-71.
16. Liu K., Cheng Y.-Q., and Yang J.-Y. Algebraic feature extraction for image recognition based on an optimal discriminant criterion. Pattern Recognition 26 6 (1993) 903-911
17. Pang S, Ozawa S, Kasabov N. Chunk incremental LDA computing on data streams. Adv Neural Networks LNCS. Vol. 3497, 2005. p. 51-6.
18. Pang S., Ozawa S., and Kasabov N. Incremental linear discriminant analysis for classification of data streams. IEEE Trans Syst Man Cybern - Part B 35 5 (2005) 905-914
19. Swets D.L., and Weng J. Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell 18 8 (1996) 831-836
20. Turk MA, Pentland AP. Face recognition using eigenfaces. In: Proceedings of IEEE international conference on the computer vision and pattern recognition, June 1991. p. 586-91.
21. Uray M, Skocaj D, Roth PM, Bischof H, Leonardis A. Incremental LDA learning by combining reconstructive and discriminative approaches. In: Proceedings of the br mach vision conf, 2007.
22. Wang X, Tang X. Dual-space linear discriminant analysis for face recognition. In: Proceedings of the IEEE international conference on computer vision and pattern recognition, 2004, p. 664-9
23. Wang X, Tang X. Random sampling LDA for face recognition. In: Proceedings of the IEEE international conference on computer vision and pattern recognition, 2004, p. 259-65
24. Wang J-G, Kong H, Sung E, Yau W-Y, Teoh EK. Fusion of appearance image and passive stereo depth map for face recognition based on the bilateral 2DLDA. EURASIP J Image Video Process 2007;2007(2):6.
25. Xiong H., Swamy M.N.S., and Ahmad M.O. Two-dimensional FLD for face recognition. Pattern Recognition 38 7 (2005) 1121-1124
26. Ye J, Janardan R, Li Q. Two dimensional linear discriminant analysis. In: Proceedings of the neural information processing systems (NIPS), 2004a, p. 1569-76
27. Ye J, Li Q, Xiong H, Park H, Janardan R, Kumar V. IDR/QR: an incremental dimension reduction algorithm via QR decomposition. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (KDD), 2004b. p 364-73.
28. Yu H., and Yang J. A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recognition 34 10 (2001) 2067-2070
29. Zhao W. Discriminant component analysis for face recognition. In: Proceedings of the international conference on pattern recognition, 2000. p. 818-21.
30. Zhao H., and Yuen P.C. Incremental linear discriminant analysis for face recognition. IEEE Trans Syst Man Cybern B Cybern 38 1 (2008) 210-221