Facial Expression Recognition Based On Complex Wavelet Transform

Facial expression holds rich human behavior information and plays a leading part in human non-verbal communication. In the field of Human-Computer Interaction, more attentions have been paid on it in recent decades. If computer is able to recognize and understand human emotions better, human and computer interaction will be greatly improved and serves human better.In this paper, the literatures home and abroad about facial expression recognition are in deep research and analysis, and it shows that feature extraction gets much importance in recognition system. We focus a number of issues about facial expression recognition and make research on facial expression feature extraction algorithms. Furthermore, some improvements of feature extraction methods are proposed. Experiments prove the efficiency of the proposed algorithms. The main contributions of this paper are:Firstly, we improve the existing method of supervised spectral analysis. Another two Laplacian matrixes are introduced to get better performance, and experiments are done to verify its effectiveness.Secondly, supervised spectral analysis based on dual-tree complex wavelet transform is proposed. We introduce the dual-tree complex wavelet transform and bring forward some improvement. In virtue of the attractive properties such as shift-invariance, direction selectivity, perfect reconstruction and efficient computing, we decompose the image to 4 scales to extract expression feature.6 sub-images oriented at 6 different directions will be obtained at each scale, and it shows its multi-scale and multi-direction resolution ability. Meanwhile subtle and local characteristics of image will be presented better. Combined with supervised spectral analysis, the dual-tree complex wavelet transform enhances the recognition performance remarkably. Experiments on the JAFFE database and CK database show its effectiveness.Thirdly, the dual-tree complex wavelet transform and single-tree complex wavelet transform are compared with each other. Furthermore, we propose the method of supervised spectral analysis based on the dual-tree complex wavelet transform. By comparison of these two complex wavelet transforms, we learned that though the ability to present image features is very similar, the single-tree complex wavelet transform is more complex to compute.Fourthly, a demonstration system for facial expression recognition is built to show our achievement, and an experimental platform is provided for the follow-up study at the same time.