Facial expression analysis by kernel eigenspace method based on class features (KEMC) using non-linear basis for separation of expression-classes

Proceedings - International Conference on Image Processing, ICIP

Volumn 5, Issue , 2004, Pages 1409-1412

  Kosaka, Y ^a   Kotani, K ^a  

^a JAPAN ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; FEATURE EXTRACTION; MAN MACHINE SYSTEMS; MAPPING; MAPS; VECTORS;

FACIAL EXPRESSION ANALYSIS; KERNEL EIGENSPACE METHOD; LINEAR TRANSFORMATION; NONLINEAR MAPPING;

FACE RECOGNITION;

EID: 20444478695     PISSN: 15224880     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (4)

1. T. Kurozumi, Y. Shinza, Y. Kenmochi, K. Kotani, "Facial individuality and expression analysis by eigenspace method based on class features or multiple discriminant analysis", Proc. of 1999 IEEE ICIP, Vol. 1, pp.648-652, 1999.
2. B. Schölkopf, A. Smola and K. R. Müller, "Nonlinera Component Analysis as a Kernel Eigenvalue Problem", Neural Computation, vol. 10, pp. 1299-1319, 1998.
3. M. -H. Yang, "Kernel eigenfaces vs. kernel fisher-faces: face recognition using kernel methods", Proc. of Int'l Conf. on Automatic Face and Gesture Recognition, pp. 215-220(2002)
4. Q. Liu, R. Huang, H. Lu, and S. Ma, "Face recognition using kernel based Fisher discriminant analysis", Proc. of Int'l Conf. on Automatic Face and Gesture Recognition, pp. 197-201(2001)