A Feature Extraction Algorithm Based on 2D Complexity of Gabor Wavelets Transform For Facial Expression Recognition

Facial expression recognition is one of challenging topic in image processing. In this paper, a new feature extraction algorithm is proposed for facial expression recognition, in which Gabor filter is combined with 2D complexity for feature extraction. In order to obtain information of texture of expression in a static gray image, the image is transformed to sub images by Gabor wavelet after considerable pretreatment, and then the complexities of sub images are calculated. Fast Principle component analysis (Fast-Pca) is used to reduce the dimensionality of 2D complexity of the sub images and the effectiveness of characteristic vector is tested through an learning vector quantization (LVQ) classifier. The proposed feature extraction algorithm has been successfully applied to the Japanese Female Facial Expression (JAFFE) database with 213 frontal images corresponding 10 different subjects. The images are acquired under variable illumination. Experimental results show that the proposed algorithm obtains low-dimension of features compared with traditional method and expression recognition accuracy is improved.