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Feature Synthesis using
Genetic Programming for Face Expression Recognition
Presented by: Jingang Yu
Abstract:
A novel genetically-inspired learning method is proposed
for face expression recognition (FER) in learning 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
composite operators is feature vectors used for FER. Our experimental
results show that our approach can find good composite operators
to effectively extract useful features.
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