Automatic learning of appearance face models

Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems

Volumn 2001-January, Issue January, 2001, Pages 32-39

  Torre, Fernando De La ^a  

^a UNIVERSITAT RAMON LLULL   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMATION; COMPUTER VISION; FACE RECOGNITION; GESTURE RECOGNITION; HUMAN COMPUTER INTERACTION; IMAGE PROCESSING; IMAGE REGISTRATION; INTERACTIVE COMPUTER SYSTEMS; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; STOCHASTIC SYSTEMS; VIDEO CONFERENCING;

AUTOMATIC-LEARNING; CONTINUOUS OPTIMIZATION PROBLEMS; DETERMINISTIC TECHNIQUE; LEARNING PROCESS; REAL-TIME VIDEO CONFERENCING; ROBUST ALGORITHM; SUBPIXEL ACCURACY; VISION RESEARCH;

REAL TIME SYSTEMS;

EID: 0006496916     PISSN: 15301044     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/RATFG.2001.938907     Document Type: Conference Paper

References (23)

1. M. Black, D. J. Fleet, and Y. Yacoob. Robustly estimating changes in image appearance. CVZU, 78(1):8-31, 2000.
2. M. Black and Y. Yacoob. Recognizing facial expressions in image sequences using local parameterized models of image motion. ZJCV, 25(1):23-48, 1997.
3. M. J. Black and A. D. Jepson. Eigentracking: Robust matching and tracking of objects using view-based representation. IJCV, 25(19):57-92, 1996.
4. A. Blake and M. Isard. Active Contours. SpringerVerlag, 1998.
5. A. E. Bryson and Y. C. HO. Applied Optimal Control. Hemispheric Publications, 1975.
6. T. Cootes, G. J. Edwards, and C. Taylor. Active appearance models. In ECCV, 1998.
7. F. De la Torre, S. Gong, and S. McKenna. View-based adaptive affine alignment. In FG, 1998.
8. F. De la Torre, Y. Yacoob, and L. Davies. A probabilistic framework for rigid and non-rigid appearance based tracking and recognition. In FG, 2000.
9. F. De la Torre, J. Vitria, P. Radeva and J. Melenchon. Eigsnfiltering for Flexible Eigentracking , In ICPR, 2000.
10. F. De la Torre and M. Black. Robust principal component analysis for computer vision. In ICCV, 2001.
11. B. J. Frey and N. Jojic. Transformation-invariant clustering and dimensionality reduction. Submitted to PAMZ, 2000.
12. S. Geman and D. McClure. Statistical methods for tomographic image reconstruction. Bulletin of the International Statistical Institute, LII:4:5, 1987.
13. S. Gong, S. Mckenna, and A. Psarrou. Dynamic Vision: From Images to Face Recognition. Imperial College Press, 2000.
14. F. Hampel, E. Ronchetti, P. Rousseeuw, and W. Stahel. Robust Statistics: The Approach Based on Influence Functions. Wiley, New York., 1986.
15. M. Mitchell. An Introduction to Genetic Algorithms. 'MIT Press, 1996.
16. B. Moghaddam and A. Pentland. Probabilistic visual learning for object representation. PAMZ, 7( 19), 1997.
17. S. K. Nayar and T. Poggio. Early Visual Learning. Oxford University Press, 1996.
18. R. P. Rao. Dynamic Appearance-Based Recognition. CVPR, 1997.
19. S. Roweis and Z. Ghahramani. A unifying review of linear gaussian models. Neural Computation, 1999.
20. H. Schweitzer. Optimal eigenfeature selection by optimal image registration. In CVPR, 1999.
21. R. Shumway and D. Stoffer. An approach to time series smoothing and forecasting using the em algorithm. J. Time Series Analysis, 3(4):253-264, 1982.
22. L. Sirovich and M. Kirby. Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. A, 4(3):519-524, March 1987.
23. M. Turk and A. Pentland. Eigenfaces for recognition. Journal Cognitive Neuroscience, 3(1):71-86, 1991.