Face recognition from video

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

Volumn 3954 LNCS, Issue , 2006, Pages 27-40

  Arandjelović, Ognjen ^a   Cipolla, Roberto ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; ROBUSTNESS (CONTROL SYSTEMS); STATISTICAL METHODS; VIDEO SIGNAL PROCESSING;

COMMERCIAL SOFTWARE; UNCONSTRAINED SETUP; VIDEO SEQUENCES;

FACE RECOGNITION;

EID: 33745854103     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11744085_3     Document Type: Conference Paper

References (47)

1. Y Adini, Y. Moses, and S. Ullman. Face recognition: The problem of compensating for changes in illumination direction. PAMI, 19(7):721-732, 1997.
2. O. Arandjelović and R. Cipolla. A new look at filtering techniques for illumination invariance in automatic face recogition. FG, 2006.
3. O. Arandjelović, G. Shakhnarovich, J. Fisher, R. Cipolla, and T. Darrell. Face recognition with image sets using manifold density divergence. CVPR, 2005.
4. O. Arandjelović and A. Zisserman. Automatic face recognition for film character retrieval in feature-length films. CVPR, 1:860-867, 2005.
5. W. A. Barrett. A survey of face recognition algorithms and testing results. Systems and Computers, 1:301-305, 1998.
6. BBC. Doubts over passport face scans. BBC Online, UK Edition, October 2004.
7. P. N. Belhumeur and D. J. Kriegman. What is the set of images of an object under all possible illumination conditions? IJCV, 28(3):245-260, 1998.
8. T. L. Berg, A. C. Berg, J. Edwards, M. Maire, R. White, Y. W. Teh, E. Learned-Miller, and D.A Forsyth. Names and faces in the news. CVPR, 2:848-854, 2004.
9. M. Bichsel and A. P. Pentland. Human face recognition and the face image set's topology. Computer Vision, Graphics and Image Processing, 59(2):254-261, 1994.
10. V. Blanz and T. Vetter. Face recognition based on fitting a 3D morphable model. PAMI, 25(9): 1063-1074, 2003.
11. I. Craw, N. P. Costen, T. Kato, and S. Akamatsu. How should we represent faces for automatic recognition? PAMI, 21:725-736, 1999.
12. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wily & Sons, Inc., New York, 2nd edition, 2000.
13. M. Everingham and A. Zisserman. Automated person identification in video. CIVR, pages 289-298, 2004.
14. A. Fitzgibbon and A. Zisserman. On affine invariant clustering and automatic cast listing in movies. ECCV, pages 304-320, 2002.
15. K. Fukui and O. Yamaguchi. Facial feature point extraction method based on combination of shape extraction and pattern matching. Systems and Computers in Japan, 29(6):2170-2177, 1998.
16. K. Fukui and O. Yamaguchi. Face recognition using multi-viewpoint patterns for robot vision. Int'l Symp. of Robotics Research, 2003.
17. D. M. Gavrila. Pedestrian detection from a moving vehicle. ECCV, 2:37-49, 2000.
18. A. S. Georghiades, D. J. Kriegman, and P. N Belhumeur. Illumination cones for recognition under variable lighting: Faces. CVPR, pages 52-59, 1998.
19. D. O. Gorodnichy, Associative neural networks as means for low-resolution video-based recognition. International Joint Conference on Neural Networks, 2005.
20. R. Gross, I. Matthews, and S. Baker. Generic vs. person specific active appearance models. BMVC, 2004.
21. P. Hall, D. Marshall, and R. Martin. Merging and splitting eigenspace models. PAMI, 22(9): 1042-1049, 2000.
22. Idetitix. Faceit. http://www.FaceIt.com/.
23. B. Kepenekci. Face Recognition Using Gabor Wavelet Transform. PhD thesis, The Middle East Technical University, 2001.
24. T. Kim, O. Arandjelović, and R. Cipolla. Learning over sets using boosted manifold principal angles (BoMPA). BMVC, 2005. (to appear).
25. R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel. A variational framework for retinex. IJCV, 52(1):7-23, 2003.
26. K. Lee and D. Kriegman. Online learning of probabilistic appearance manifolds for video-based recognition and tracking. CVPR, 1:852-859, 2005.
27. K. Lee, M. Yang, and D. Kriegman. Video-based face recognition using probabilistic appearance manifolds. CVPR, 1:313-320, 2003.
28. Y. Li, S. Gong, and H. Liddell. Modelling faces dynamically across views and over time. ICCV, 1:554-559, 2001.
29. X. Liu and T. Chen. Video-based face recognition using adaptive hidden Markov models. CVPR, 1:340-345, 2003.
30. H. Murase and S. Nayar. Visual learning and recognition of 3-D objects from appearance. IJCV, 14:5-24, 1995.
31. S. Palanivel, B. S. Venkatesh, and B Yegnanarayana. Real time face recognition system using autoassociative neural network models. ASSP, 2:833-836, 2003.
32. A. Pentland, B. Moghaddam, and T. Starner. View-based and modular eigenspaces for face recognition, CVPR, 84-91, 1994.
33. P. J. Phillips, P. Grother, R. J. Micheals, D. M. Blackburn, E. Tabassi, and J. M. Bone. FRVT 2002: Overview and summary. Technical report, National Institute of Justice, March 2003.
34. T. Riklin-Raviv and A. Shashua. The quotient image: Class based re-rendering and recognition with varying illuminations. PAMI, 23(2):219-139, 2001.
35. G. Shakhnarovich, J. W. Fisher, and T. Darrel. Face recognition from long-term observations. ECCV, 3:851-868, 2002.
36. T. Sim and S. Zhang. Exploring face space. Face Processing in Video, 2004.
37. J. Sivic, M. Everingham, and A. Zisserman. Person spotting: video shot retrieval for face sets. CIVR, 2005.
38. B. Stenger, A. Thayananthan, P. Torr, and R. Cipolla. Filtering using a tree-based estimator. ICCV, 2:1063-1070, 2003.
39. J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323, 2000.
40. M. E. Tipping and C. M. Bishop. Mixtures of probabilistic principal component analyzers. Neural Computation, 11(2):443-482, 1999.
41. Toshiba. Facepass. http://www.toshiba.co.jp/mmlab/tech/w31e.htm.
42. P. Viola and M. Jones. Robust real-time face detection. IJCV, 57(2), 2004.
43. H. Wang, S. Z. Li, and Y. Wang. Face recognition under varying lighting conditions using self quotient image. FG, pages 819-824, 2004.
44. X. Wang and X. Tang. Unified subspace analysis for face recognition. ICCV, 2003.
45. L. Wolf and A. Shashua. Learning over sets using kernel principal angles. JMLR, 4(10):913-931, 2003.
46. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld. Face recognition: A literature survey. ACM Computing Surveys, 35(4):399-458, 2004.
47. S. Zhou, V. Krueger, and R. Chellappa. Probabilistic recognition of human faces from video. Computer Vision and Image Understanding, 91(1):214-245, 2003.