New feature extraction approaches for face recognition

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

Volumn 3809 LNAI, Issue , 2005, Pages 489-497

  Nhat, Vo Dinh Minh ^a   Lee, Sungyoung ^a  

^a Kyung Hee University   (South Korea)

Author keywords

Face Recognition; Linear Discriminant Analysis (LDA); Principle component analysis (PCA)

Indexed keywords

COMPUTATION THEORY; FEATURE EXTRACTION; IMAGE ANALYSIS; MATHEMATICAL TRANSFORMATIONS; NUMERICAL ANALYSIS; PRINCIPAL COMPONENT ANALYSIS;

DISCRIMINANT INFORMATION; IMAGE REPRESENTATION; LINEAR DISCRIMINANT ANALYSIS (LDA); VECTOR ANALYSIS;

FACE RECOGNITION;

EID: 33745633393     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11589990_51     Document Type: Conference Paper

References (17)

1. M. Turk, A. Pentland : Eigenfaces for face recognition. Int. J. Cog. Neurosci., vol. 3, (1991) 71-86.
2. 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.
3. D. L. Swets, J. J. Weng: Using discrimination eigenfeatures for image retrieval. IEEE Trans. Pattern Anal. Machine Intell., vol. 18 (1996) 831-836.
4. P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman: Eigenfaces vs. fisherface: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Machine Intell., vol. 19 (1997) 711-720.
5. H. Yu, J. Yang: A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recognit., vol. 34 (2001) 2067-2070.
6. M. Loog, R. P. W. Duin, R. Haeb-Umbach: Multiclass linear dimension reduction by weighted pairwise fisher criteria. IEEE Trans. Pattern Anal. Machine Intell., vol. 23 (2001) 762-766.
7. A. M. Martinez, A. C. Kak: PCA versus LDA. IEEE Trans. Pattern Anal. Machine Intell., vol. 23 (2001) 228-233.
8. D. H. Foley, J. W. Sammon: An optimal set of discrimination vectors. IEEE Trans. Comput., vol. C-24 (1975) 281-289.
9. [9] R. A. Fisher, "The use of multiple measurements in taxonomic problems," Ann. Eugenics, vol. 7, pp. 178-188, 1936.
10. Rui Huang, Qingshan Liu, Hanqing Lu, Songde Ma: Solving the small sample size problem of LDA. Pattern Recognition, 2002. Proceedings. 16th International Conference on, Vol 3 (2002) 29 - 32.
11. C. Liu, H. Wechsler: Robust coding scheme for indexing and retrieval from large face databases. IEEE Trans. Image Processing, vol. 9 (2000) 132-137.
12. Chengjun Liu, Wechsler H.: A shape- and texture-based enhanced Fisher classifier for face recognition. IEEE Trans. Image Processing, vol. 10 (2001) 598-608.
13. L. Chen, H. M. Liao, M. Ko, J. Lin, G. Yu: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognit., vol. 33 (2000) 1713-1726.
14. Cevikalp, H., Neamtu, M., Wilkes. M., Barkana, A.: Discriminative common vectors for face recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 27 (2005) 4-13.
15. Jian Yang, Zhang, D., Frangi, A.F., Jing-yu Yang: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 26 (2004) 131 - 137
16. Ming Li, Baozong Yuan : 2D-LDA: A statistical linear discriminant analysis for image matrix. Pattern Recognition Letters, Vol. 26, Issue 5 (2005) 527-532.
17. "The ORL face database" http://www.uk.research.att.com/ facedatabase.html.