Recognising trajectories of facial identities using kernel discriminant analysis

Image and Vision Computing

Volumn 21, Issue 13-14, 2003, Pages 1077-1086

  Li, Yongmin ^a   Gong, Shaogang ^b   Liddell, Heather ^b  

^a MARTLESHAM HEATH   (United Kingdom)

^b QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Face recognition; Identity surfaces; Kernel discriminant analysis; Multi view face models

Indexed keywords

ALGORITHMS; FACE RECOGNITION; MATHEMATICAL MODELS; PROBLEM SOLVING; VISION;

IDENTITY SURFACES; KERNEL DISCRIMINANT ANALYSIS;

IMAGE ANALYSIS;

EID: 0344084405     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2003.08.010     Document Type: Conference Paper

References (24)

1. Baudat G., Anouar F. Generalized discriminant analysis using a kernel approach. Neural Computation. 12:2000;2385-2404.
2. Bruce V., Burton A., Hancock P. Comparisons between human and computer recognition of faces. in: IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan. 1998;. pp. 408-413.
3. Burges C.J.C. Simplified support vector decision rules. Saitta L. Proceedings, 13th International Conference on Machine Learning. 1996;71-77 Morgan Kaufmann, San Mateo, CA.
4. Burges C.J.C., Scholkopf B. Improving the accuracy and speed of support vector learning machines. Mozer M., Jordan M., Petsche T. Advances in Neural Information Processing Systems 9. 1997;375-381 MIT Press, Cambridge, MA.
5. Cootes T., Edwards G., Taylor C. Active appearance models. in: European Conference on Computer Vision, Freiburg, Germany, vol. 2. 1998;. pp. 484-498.
6. Cootes T., Taylor C., Cooper D., Graham J. Active shape models-their training and application. Computer Vision and Image Understanding. 61:(1):1995;38-59.
7. Edwards G., Taylor C., Cootes T. Learning to identify and track faces in sequences. in: IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan. 1998;. pp. 260-267.
8. Fukunaga K. Introduction to Statistical Pattern Recognition. 1972;Academic Press, New York.
9. Gong S., McKenna S., Psarrou A. Dynamic Vision: From Images to Face Recognition. 2000;World Scientific Publishing/Imperial College Press.
10. Gong S., Psarrou A., Katsouli I., Palavouzis P. Tracking and recognition of face sequences. in: European Workshop on Combined Real and Synthetic Image Processing for Broadcast and Video Production, Hamburg, Germany. 1994;. pp. 96-112.
11. Knight B., Johnston A. The role of movement in face recognition. Visual Cognition. 4:1997;265-274.
12. Lades M., Vorbruggen J., Buhmann J., Lange J., Malsburg C., Wurtz R., Konen W. Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions on Computers. 42:(3):1993;300-311.
13. Li Y., Gong S., Liddell H. Modelling faces dynamically across views and over time. in: IEEE International Conference on Computer Vision, Vancouver, Canada. 2001;. pp. 554-559.
14. Mika S., Ratsch G., Weston J., Scholkopf B., Muller K. Fisher discriminant analysis with kernels. in: IEEE Neural Networks for Signal Processing Workshop. 1999;41-48.
15. Roth V., Steinhage V. Nonlinear discriminant analysis using kernel functions. Solla S., Leen T., Müller K.-R. Advances in Neural Information Processing Systems 12. 1999;568-574 MIT Press, Cambridge, MA.
16. Scholkopf B. Support Vector Learning. 1997;R. Oldenbourg Verlag, Munich.
17. Scholkopf B., Smola A., Muller K.-R. Kernel principal component analysis. Gerstner W., Germond A., Hasler M., Nicoud J.-D. Artificial Neural Networks - ICANN'97. Lecture Notes in Computer Science. 1997;583-588 Springer, Berlin.
18. Sirovich L., Kirby M. Low-dimensional procedure for the characterization of human faces. Journal of Optical Society of America. 4:1987;519-524.
19. Swets D., Weng J. Using discriminant eigenfeatures for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence. 18:(8):1996;831-836.
20. Turk M., Pentland A. Eigenfaces for recognition. Journal of Cognitive Neuroscience. 3:(1):1991;71-86.
21. R. Vanderbei, Loqo: an interior point code for quadratic programming, Technical report, Princeton University, Technical Report SOR 94-15, 1994.
22. Vapnik V. The Nature of Statistical Learning Theory. 1995;Springer, New York.
23. Vapnik V. Statistical Learning Theory. 1998;Wiley, New York.
24. Vetter T., Poggio T. Linear object classes and image synthesis from a single example image. IEEE Transactions on Patternx Analysis and Machine Intelligence. 19:(7):1997;733-742.