Facial emotion recognition via discrete wavelet transform, principal component analysis, and cat swarm optimization

Abstract

Facial emotion recognition is important in many academic and industrial applications. In this paper, our team proposed a novel facial emotion recognition method. First, we used discrete wavelet transform to extract wavelet coefficients from facial images. Second, principal component analysis was utilized to reduce the features. Third, a single-hidden-layer neural network was used as the classifier. Finally and most importantly, we introduced the cat swarm optimization to train the weights and biases of the classifier. The ten-fold stratified cross validation showed cat swarm optimization method achieved an overall accuracy of 89.49 ± 0.76%. It was better than genetic algorithm, particle swarm optimization, and time-varying-acceleration-coefficient particle swarm optimization. Besides, our facial emotion recognition system was better than two state-of-the-art approaches.