Boosting local feature based classifiers for face recognition

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn 2004-January, Issue January, 2004, Pages

  Zhang, Lei ^a   Li, Stan Z ^b   Qu, Zhi Yi ^a   Huang, Xiangsheng ^c  

^a LANZHOU UNIVERSITY   (China)

^b MICROSOFT RESEARCH ASIA   (China)

^c INSTITUTE OF AUTOMATION   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER VISION; GABOR FILTERS; OBJECT DETECTION; OBJECT RECOGNITION;

CELL PAIRS; FEATURE-BASED; GABOR FEATURE BASED CLASSIFIERS; INTRA CLASS; LOCAL FEATURE; MULTICLASS PROBLEM; PHASE FEATURES; QUADRATURE PHASE; SIMPLE CELL; WEAK CLASSIFIERS;

FACE RECOGNITION;

EID: 84932617823     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2004.309     Document Type: Conference Paper

References (19)

1. M. S. Bartlett , H. M. Lades, and T. J. Sejnowski. Independent component representations for face recognition. Proceedings of the SPIE, Conference on Human Vision and Electronic Imaging III, 3299:528-539, 1998.
2. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman. 'Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711-720, July 1997.
3. B.Schiele and J.L.Crowley. Recognition without correspondence using multidimensional receptive field histograms. International Journal of Computer Vision, 36:31-52, 2000.
4. Y. Freund and R. Schapire. 'A decision-theoretic generalization of on-line learning and an application to boosting ' . Journal of Computer and System Sciences, 55(1):119-139, August 1997.
5. J.G.Daugman. Two-dimensional spectral analysis of cortical receptive field profiles. Vision Research, 20:847-856, 1980.
6. J.G.Daugman. Quadrature-phase simple-cell pairs are appropriately described in complex analytic from. Journal Opt.Soc.Amer, 1992.
7. M. Lades, J. Vorbruggen, J. Buhmann, J. Lange, C. Von Der Malsburg,W. R.P., andW. Konen. Distortion invariant object recognition i n the dynamic link architecture. IEEE Transactions on Computers, 42:300-311, 1993.
8. S. Lawrence, C. L. Giles, A. Tsoi, and A. Back. 'Face recognition: A convolutional neural network approach ' . IEEE Transactions on Neural Networks, 8(1):98-113, 1997.
9. S. Z. Li and J. Lu. 'Face recognition using the nearest feature line method '. IEEE Transactions on Neural Networks, 10(2):439-443, March 1999.
10. C. Liu and H. Wechsler. ' Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition' . IEEE Transactions on Image Processing, 11(4):467-476, 2002.
11. B. Moghaddam. Principal manifolds and bayesian subspaces for visual recognition. In International Conference on Computer Vision (ICCV'99), pages 1131-1136, September 1999.
12. B. Moghaddam and A. Pentland. Beyond euclidean eigenspaces:bayesian matching for visian recognition.
13. Y. Moses, Y. Adini, and S. Ullman. 'Face recognition : The problem of compensating for changes in illumination direction' . In Proceedings of the European Conference on Computer Vision, volume A, pages 286-296, 1994.
14. P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss. The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(10):1090-1104, 2000.
15. D. P.W.Ellis and J. A .Bilmes. Using mutual information to design feature combinations. Int. Conference on Spoken Language Processing, 3:79-82, 2000.
16. M. A. Turk and A. P. Pentland. 'Eigenfaces for recognition ' . Journal of Cognitive Neuroscience, 3(1):71-86, March 1991.
17. P. Viola and M. Jones. 'Robust real time object detection ' . In IEEE ICCV Workshop on Statistical and Computational Theories of Vision, Vancouver, Canada, July 13 2001.
18. L. Wiskott, J. Fellous, N. Kruger, and C. V. malsburg. ' face recognition by elastic bunch graph matching' . IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):775-779, 1997.
19. W. S. Yambor. Analysis of pca-based and fisher discriminant-based image recognition algorithms. M.s., Colorado State University, July 2000. Technical Report CS-00-103, Computer Science.