Face distributions in similarity space under varying head pose

Image and Vision Computing

Volumn 19, Issue 12, 2001, Pages 807-819

  Sherrah, J ^a   Gong, S ^a   Ong, E J ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Face recognition; Gabor filters; Head pose estimation; Similarity representation

Indexed keywords

EID: 0347868848     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0262-8856(00)00096-2     Document Type: Article

References (25)

1. C. Bishop, Neural Networks for Pattern Recognition, Cambridge University Press, Cambridge, MA, 1995.
2. S. Duvdevani-Bar, S. Edelman, A.J. Howell, H. Buxton, A similarity-based method for the generalisation of face recognition over pose and expression, IEEE International Conference on Face and Gesture Recognition, Nara, Japan, April 1998, pp. 118-123.
3. S. Edelman, Representation is representation of similarities, Behavioral and Brain Sciences 21 (1998) 449-498.
4. S. Edelman, Representation and Recognition in Vision, MIT Press, Cambridge, MA, 1999.
5. K. Etemad, R. Chellappa, Discriminant analysis for recognition of human face images, Optical Society of America 14 (8) (1997) 1724-1733.
6. W.T. Freeman, E.H. Adelson, The design and use of steerable filters, IEEE Transactions on Pattern Analysis and Machine Intelligence 13 (9) (1991) 891-906.
7. S. Gong, S. McKenna, J. Collins, An investigation into face pose distributions, IEEE International Conference on Face and Gesture Recognition, Vermont, USA, October 1996, pp. 265-270.
8. S. Gong, E. Ong, P. Loft, Appearance-based face recognition under large rotations in depth, Asian Conference on Computer Vision, Hong Kong, vol. 2, IEEE Press, New York, 1998 (pp. 679-686).
9. S. Gong, E. Ong, S. McKenna, Learning to associate faces across views in vector space of similarities to prototypes, British Machine Vision Conference, Southampton, UK, vol. 1, 1998, pp. 54-64.
10. N. Kruger, M. Potzsch, C. Von der Malsburg, Determination of face position and pose with a learned representation based on labelled graphs, Image and Vision Computing 15 (1997) 665-673.
11. Y. Li, S. Gong, H. Liddell, Support vector regression and classification based multi-view face detection and recognition, Proceedings of the Fourth International Conference on Automatic Face and Gesture Recognition, Grenoble, France, March 2000, IEEE Computer Society, New York, 2000 (pp. 300-305).
12. S. McKenna, S. Gong, Tracking faces, IEEE International Conference on Face and Gesture Recognition, Killington, Vermont, US, October 1996, pp. 271-277.
13. B. Moghaddam, A. Pentland, Probabilistic visual learning for object representation, IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (7) (1997) 696-710.
14. J. Ng, S. Gong, Performing multi-view face detection and pose estimation using a composite support vector machine across the view sphere, Proceedings of the IEEE International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time SystemsKerkyra, Greece, Kerkyra, Greece, IEEE Press, New York, 1999.
15. E. Osuna, R. Freund, F. Girosi, Training support vector machines: an application to face detection, IEEE Conference on Computer Vision and Pattern Recognition, 1997, pp. 130-136.
16. A. Pentland, B. Moghaddam, T. Starner, View-based and modular eigenspaces for face recognition, IEEE Conference on Computer Vision and Pattern Recognition, 1994, pp. 84-91.
17. W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Numerical Recipes in C, 2nd ed., Cambridge University Press, Cambridge, MA, 1992.
18. Y. Raja, S.J. McKenna, S. Gong, Tracking and segmenting people in varying lighting conditions using colour, Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan, 1998, pp. 228-233.
19. R.P.N. Rao, D.H. Ballard, Natural basis functions and topographic memory for face recognition, International Joint Conference on Artificial Intelligence, Montreal 1 (1995) 10-17.
20. H. Rowley, S. Baluja, T. Kanade, Neural network-based face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (1) (1998) 23-38.
21. K. Sung, T. Poggio, Example-based learning for view-based human face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (1) (1998) 39-51.
22. R.Y. Tsai, A. versatile, camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses, IEEE Journal of Robotics and Automation RA-3 (4) (1987) 323-344.
23. M. Turk, A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience 3 (1) (1991) 71-86.
24. T. Vetter, T. Poggio, Linear object classes and image synthesis from a single example image, IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (7) (1997) 733-742.
25. R.P. Würtz, Multilayer Dynamic Link Networks for Establishing Image Point Correspondences and Visual Object Recognition, Verlag Harri Deutsch, 1994.