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Feature Synthesis Using Genetic Programming for Face Expression Recognition
Bir Bhanu, Jiangang Yu, Xuejun Tan, and Yingqiang Lin
Center for research in intelligent systems, University of California, Riverside CA 92521-0425, USAbhanu@cris.ucr.edu
jyu@cris.ucr.edu
xtan@cris.ucr.edu
yqlin@cris.ucr.edu
Abstract. In this paper a novel genetically-inspired learning method is proposed for face expression recognition (FER) in visible images. Unlike current research for FER that generally uses visually meaningful feature, we proposed a Genetic Programming based technique, which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. In this approach, the output of the learned composite operator is a feature vector that is used for FER. The experimental results show that our approach can find good composite operators to effectively extract useful features.
LNCS 3103, p. 896 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004