This paper proposes a novel facial expression recognition approach based on two sets of features extracted from the face images: texture features and global appearance features. The first set is obtained by using the extended local binary patterns in both intensity and gradient maps and computing the Tsallis entropy of the Gabor filtered responses. The second set of features is obtained by performing nullspace based linear discriminant analysis on the training face images. The proposed method is evaluated by extensive experiments on the JAFFE database, and compared with two widely used facial expression recognition approaches. Experimental results show that the proposed approach maintains high recognition rate in a wide range of resolution levels and outperforms the other alternative methods.

To segment a whole object from an image is an essential and challenging task in image processing. In this paper, we propose a hybrid segmentation algorithm which combines prior shape information with normalized cut. With the help of shape information, we can utilize normalized cut to correctly segment the target whose boundary may be corrupted by noise or outliers. At the same time, we introduce the use of segmentation results of the normalized cut to guide the shape model, and thus avoid searching the shape space. The proposed method was demonstrated to be effective by our experiments on both synthetic and real data.