Facial expression recognition based on the daul-tree complex wavelet transform and supervised spectral analysis

A novel feature extraction method is proposed in this paper named DTCW-SA which is based on the dual-tree complex wavelet transform and supervised spectral analysis for facial expression recognition. Although holding the property of multi-resolution entirely, compared with traditional wavelet and Gabor transform, the attractive characteristics of DT-CWT are better orientation selectivity, approximate shift-invariance and lower redundancy. Different with existing DT-CWT, we extend images to appropriate size by interpolation before transform instead of copying the values of last row or column when decomposition of each scale. What´s more, the method of supervised spectral analysis can dig nonlinear information hidden in the data. Experiments on JAFFE database and CK database illustrate the efficiency of DTCW-SA, and the highest average rate of six expressions reaches 97.8% on CK database.