Weight-Adapted Convolution Neural Network for Facial Expression Recognition

Abstract

The weight-adapted convolution neural network (WACNN) is proposed to extract discriminative expression representations for recognizing facial expression. It aims to make good use of the convolution neural network’s potential performance in avoiding local optimal and speeding up convergence by hybrid genetic algorithm (HGA) with optimal initial population, in such a way that it realizes deep and global emotion understanding in human-robot interaction. Moreover, the idea of novelty search is introduced to solve the deception problem in the HGA, which can expend the search space to help genetic algorithm jump out of local optimum and optimize large-scale parameters. In the proposal, the facial expression image preprocessing is conducted first, then the low-level expression features are extracted by using principal component analysis. Finally, the high-level expression semantic features are extracted and recognized by WACNN which is optimized by HGA.