An Efficient Expression Recognition Method Using Contourlet Transform

The paper proposes an efficient human expression recognition method in transformed domain using discrete Contourlet transform (DCT). DCT represents smooth contour information in different directions reflecting human perception and so relevant to recognize facial expressions more accurately. Each face is decomposed using discrete Contourlet transform up to fourth level and coefficients of high frequency and low frequency components with varied scales and angles are obtained. Logarithmic invariant moments of directional coefficients at different levels and histogram analysis are used to build the feature vectors for classification. To reduce dimension of the feature vectors, directional subbands are selected by analyzing the entropy of the feature vectors. Support Vector Machine (SVM) is applied to classify different expressions using the proposed method. Experimental results show promising performance applied on JAFFE and Cohn-Kanade database compare to other transformed domain methods.