An enhanced subspace method for face recognition

Pattern Recognition Letters

Volumn 27, Issue 1, 2006, Pages 76-84

  Franco, Annalisa ^a   Lumini, Alessandra ^a   Maio, Dario ^a   Nanni, Loris ^a  

^a UNIVERSITY OF BOLOGNA   (Italy)

Author keywords

Face recognition; Mixture of linear subspaces; Non linear Fisher transform

Indexed keywords

AGING OF MATERIALS; BENCHMARKING; DATABASE SYSTEMS; MATHEMATICAL TRANSFORMATIONS; NONLINEAR SYSTEMS; ROBUSTNESS (CONTROL SYSTEMS);

MIXTURE OF LINEAR SUBSPACES; NON-LINEAR FISCHER TRANSFORM;

FACE RECOGNITION;

EID: 27744438142     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2005.07.002     Document Type: Article

References (27)

1. Baek, K., Draper, B.A., 2002. Factor analysis for background suppression. In: Proc. Internat. Conf. on Pattern Recognition 2002, Quebec City, Canada, vol. 2, pp. 643-646.
2. P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection IEEE Trans. Pattern Anal. Machine Intell. 19 1997 711 720
3. J.C. Bezdek Pattern Recognition with Fuzzy Objective Function Algorithms 1981 Plenum New York
4. Cappelli, R., Maio, D., Maltoni, D., 2002. Subspace classification for face recognition. In: Proc. Workshop on Biometric Authentication 2002, Copenhagen, Denmark, pp. 133-141.
5. R.O. Duda, P.E. Hart, and D.G. Stork Pattern Classification second ed 2000 Wiley
6. Duin, R.P.W., Loog, M., Haeb-Umbach, R., 2000. Multi-class linear feature extraction by nonlinear PCA. In: Proc. Internat. Conf. on Pattern Recognition 2000, Barcelona, Spain, vol. 2, pp. 398-401.
7. Franco, A., Maltoni, D., Nanni, L., 2003. Recognition of faces: A literature survey. Biolab research report. Available from: 〈http://bias. csr.unibo.it/research/biolab〉.
8. K. Fukunaga Statistical Pattern Recognition 1990 Academic Press San Diego
9. Komleh, H.E., Chandran, V., Sridharan, S., 2001. Face recognition using fractal codes. In: Proc. Internat. Conf. on Image Processing, Thessaloniki, Greece, vol. 3, pp. 58-61.
10. M.A. Krame Nonlinear principal component analysis using auto-associative neural networks AIChE J. 37 1991 233 243
11. Li, S.Z., Xiao, G.L., Zhang, H.J., 2001. View-subspace analysis of multi-view face patterns. In: Proc. Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems 2001, Vancouver, Canada, pp. 125-132.
12. Manjunath, B.S., Chellappa, R., Von der Malsburg, C., 1992. A feature based approach to face recognition. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition 1992, Champaign, IL, pp. 373-378.
13. Moghaddam, B., Nastar, C., Pentland, A., 1996. A Bayesian similarity measure for direct image matching. In: Proc., Internat. Conf. on Pattern Recognition 1996, Vienna, Austria, vol. B, pp. 350-358.
14. H. Moon, and P.J. Phillips Computational and performance aspects of PCA-based face recognition algorithms Perception 30 2001 301 321
15. E. Oja Subspace Methods of Pattern Recognition 1983 Research Studies Press Ltd. Letchworth, England
16. K. Okada, J. Stefans, T. Maurer, H. Hong, E. Elagin, H. Neven, and C.V.D. Andmalsburg The Bochum/USC face recognition system and how it fared in the FERET phase III test H. Wechsler P.J. Phillips V. Bruce F.F. Soulie T.S. Huang Face Recognition: From Theory to Applications 1998 Springer-Verlag Berlin, Germany 186 205
17. Pan, Z., Rust, A., Bolouri, H., 2000. Image redundancy reduction for neural network classification using discrete cosine transforms. In: Proc. Internat. Joint Conf. on Neural Networks 2000, Como, Italy, vol. 3, pp. 149-154.
18. P.J. Phillips, H. Moon, S.A. Rizvi, and P.J. Rauss The FERET evaluation methodology for face recognition algorithms IEEE Trans. Pattern Anal. Machine Intell. 22 2000 1090 1104
19. C. Sanderson, and K. Paliwal Fast features for face authentication under illumination direction changes Pattern Recognition Lett. 24 2003 2409 2419
20. D.L. Swets, and J. Weng Using discriminant eigenfeatures for image retrieval IEEE Trans. Pattern Anal. Machine Intell. 18 1996 831 836
21. M. Turk, and A. Pentland Eigenfaces for recognition J. Cognitive Neurosci. 3 1 1991 71 86
22. F. Van der Heijden, R.P.W. Duin, D. De Ridder, and D.M.J. Tax Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB 2004 Wiley Hardcover
23. Vasilescu, M.A.O., Terzopoulos, D., 2002. Multilinear image analysis for facial recognition. In: Proc. Internat. Conf. on Pattern Recognition 2002, Quebec City, Canada, vol. 2, pp. 511-514.
24. Yang, M.H., Ahuja, N., Kriegman, D., 2000. Face recognition using kernel eigenfaces. In: Proc. Internat. Conf. on Image Processing 2000, Vancouver, Canada, vol. 1, pp. 37-40.
25. Zhao, W., 1999. Subspace methods in object/face recognition. In: Proc. Internat. Joint Conf. on Neural Networks 1999, Washington DC, USA, vol. 5, pp. 3260-3264.
26. Zhao, W., Chellappa, R., Krishnaswamy, A., 1998. Discriminant analysis of principal components for face recognition. In: Proc. Internat. Conf. on Automatic Face and Gesture Recognition 1998, Nara, Japan, pp. 336-341.
27. W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld Face recognition: A literature survey ACM Comput. Surv. 35 4 2003 399 458