Unsupervised recognition of multi-view face sequences based on pairwise clustering with attraction and repulsion

Computer Vision and Image Understanding

Volumn 91, Issue 1-2, 2003, Pages 22-52

  Raytchev, Bisser ^a,b   Murase, Hiroshi ^a,c  

^a NTT Communication Science Laboratories   (Japan)

^b NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

^c NAGOYA UNIVERSITY   (Japan)

Author keywords

Combinatorial optimization; Image sequences; Multi view face recognition; Pairwise clustering; Unsupervised learning

Indexed keywords

ALGORITHMS; COMPUTER VISION; IMAGE ANALYSIS;

IMAGE SEQUENCES;

FACE RECOGNITION;

EID: 0141860871     PISSN: 10773142     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1077-3142(03)00074-2     Document Type: Article

References (47)

1. A. Samal, P.A. Iyengar, Automatic recognition and analysis of human faces and facial expressions: a survey, Pattern Recognition 25 (1992) 65-77.
2. W. Zhao, R. Chellapa, A. Rosenfeld, P.J. Phillip, Face recognition: a literature survey. Available from ftp://ftp.cfar.umd.edu/TRs/CVL-Reports-2000/TR4167-zhao.ps.gz, 2000.
3. M.A. Grudin, On internal representation in face recognition systems, Pattern Recognition 33 (2000) 1161-1177.
4. H. Wechsler, P.J. Philips, V. Bruce, F.F. Soulie, T.S. Huang (Eds.), Face Recognition: From Theory to Applications, Springer, Berlin, 1998.
5. H. Ando, S. Suzuki, T. Fujita, Unsupervised visual learning of three-dimensional objects using a modular network architecture, Neural Networks 12 (1999) 1037-1053.
6. J.J. Weng, W.S. Hwang, Toward automation of learning: the state self-organization problem for a face recognizer, in: Proc. 3rd Int. Conf. on Automatic Face and Gesture Recognition, 1998, pp. 384-389.
7. B. Moghaddam, A. Pentland, Face recognition using view-based and modular eigenspaces, in: Automatic Systems for the Identification and Inspection of Humans, SPIE, vol. 2277, 1994.
8. L. Wiskott, J.M. Fellous, N. Kruger, C. von der Malsburg, Face recognition by elastic bunch graph 'matching, IEEE Trans. PAMI 19 (7) (1997) 775-779.
9. A.L. Yuille, Deformable templates for face recognition, J. Cognitive Neurosci. 3 (1) (1991) 59-70.
10. D. Beymer, T. Poggio, Face recognition from one example view, in: Proc. ICCV, 1995, pp. 500-507.
11. S.J. McKenna, S. Gong. Face recognition in dynamic scenes, in: British Machine Vision Conference, 1997.
12. G. Edwards, C. Taylor, T. Cootes, Learning to identify and track faces in sequences, in: Proc. 3th Int. Conf. on Automatic Face and Gesture Recognition, 1998, pp. 260-267.
13. S. Satoh, Comparative evaluation of face sequence matching for content-based video access, in: Proc. 4th Int. Conf. on Automatic Face and Gesture Recognition, 2000, pp. 163-168.
14. V. Krueger, S. Zhou, Exemplar-based face recognition from video, in: Proc. ECCV 2002, Lecture Notes in Computer Science, vol. 2353, 2002, pp. 732-746.
15. C. Stauffer, E. Grimson, Similarity templates for detection and recognition, in: Proc. CVPR 2001, 2001, pp. 1221-1228.
16. A.K. Jain, M.N. Murty, P.J. Flynn, Data clustering: a review, ACM Comput. Surveys 31 (3) (1999) 264-323.
17. B.S. Everitt, Cluster Analysis, Wiley, New York, 1993.
18. R. Duda, P. Hart, D. Stork, Pattern Classification. Wiley, New York, 2001.
19. A.K. Jain, R.C. Dubes, Algorithms for Clustering Data, Prentice-Hall, Englewood Cliffs, NJ, 1988.
20. B. Mirkin, Mathematical Classification and Clustering, Kluwer Academic Publishers, Dordrecht, 1996.
21. K. Rose, E. Gurewitz, G. Fox, Statistical mechanics and phase transition in clustering, Phys. Rev. Lett. 65 (1990) 945-948.
22. K. Rose, E. Gurewitz, G. Fox, Constrained clustering as an optimization method, IEEE Trans. PAMI 15 (1993) 785-794.
23. J.S. Nowlan, G.E. Hinton, Evaluation of adaptive mixtures of competing experts, in: Advances in Neural Information Processing Systems, vol. 3, 1991, pp. 774-780.
24. R.N. Dave, R. Krishnapuram, Robust clustering methods: a unified view, IEEE Trans. Fuzzy Syst. 5 (2) (1997) 270-293.
25. T. Hofmann, J. Buhmann, Pairwise data clustering by deterministic annealing, IEEE Trans. PAMI 19 (1) (1997) 1-14.
26. T.C. Hu, Combinatorial Algorithms, Addison-Wesley, Reading, MA, 1982.
27. T. Sim, R. Sukthankar, M. Mullin, S. Baluja, Memory-based face recognition for visitor identification, in: Proc. IEEE Conf. on Face and Gesture Recognition, Grenoble, 2000.
28. M.P. Dubuisson, A.K. Jain, A modified Hausdorff distance for object matching, in: Proc. ICPR, Jerusalem, Israel, 1994, pp. A566-A568.
29. W.J. Rucklidge, Efficiently locating objects using the Hausdorff distance. Int. J. Comput. Vision 24 (3) (1997) 251-270.
30. B. Raytchev, H. Murase, VQ-faces - unsupervised face recognition from image sequences, in: Proceedings ICIP, 2002, pp. II-809-II-812.
31. S. Kirkpatrick, C.D. Gelatt Jr., M.P. Vecchi, Optimization by simulated annealing, Science 220 (1983) 671-680.
32. N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, E. Teller, Equations of state calculations by fast computing machines, J. Chem, Phys. 21 (1953) 1087-1092.
33. P.J.M. van Laarhoven, E.H.L. Aarts, Simulated Annealing: Theory and Applications, Reidel, Hingham, MA, 1987.
34. S. Geman, D. Geman, Stochastic relaxation, Gibbs distribution and Bayesian restoration of images, IEEE Trans. Pattern Anal. Machine Intell. 6 (1984) 721-741.
35. J. Shi, J. Malik, Normalized cuts and image segmentation, in: Proc. CVPR, 1997, pp. 731-773.
36. F.R.K. Chung, Spectral Graph Theory, American Mathematical Society, Providence, RI, 1997.
37. J. Costeira, T. Kanade, A multibody factorization method for motion analysis, in: Proc. ICCV, 1995, pp. 1071-1076.
38. S. Sarkar, K.L. Boyer, Quantitative measures of change cased on feature organization: eigenvalues and eigenvectors, in: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1996, pp. 478-483.
39. P. Perona, W.T. Freeman, A factorization approach to grouping, in: H. Burkardt, B. Neumann (Eds.), Proc. ECCV, 1998, pp. 655-670.
40. Y. Weiss, Segmentation using eigenvectors: a unifying view, in: Proc. ICCV, 1999, pp. 975-982.
41. R. Kannan, S. Vempala, A. Vetta, On Clustering: Good, Bad and Spectral. in: Proc. 41s Annual Symposium on Foundations of Computer Science, 2000.
42. Y. Gdalyahu, D. Weinshall, M. Werman, Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping and image database organization, IEEE Trans. PAMI 23 (10) (2001) 1053-1074.
43. M. Blatt, S. Wiseman, E. Domany, Data clustering using a model granular magnet, Neural Computation 9 (1997) 1805-1842.
44. D. Karger, C. Stein, A new approach to the minimum cut problem, J. ACM 43 (1996) 601-640.
45. B. Raytchev, H. Murase, Unsupervised face recognition by associative chaining, Pattern Recognition 36 (1) (2003) 245-257.
46. E. Hjelmas, B.K. Low, Face detection: a survey, Comput. Vision Image Understanding 83 (2001) 236-274.
47. M.H. Yang, D.J. Kriegman, N. Ahuja, Detecting faces in images: a survey, IEEE Trans. Pattern Anal. Machine Intell. 24 (1) (2002) 34-58.