In this paper

we utilize the partial least squares regression (PLSR)method to solve the simultaneous recognition problem on face identity and face expression. Firstly

face features and the corresponding semantic features are extracted for each face image as the input features

where the face features are defined as the Gabor wavelet coefficients defined on several land marks of each face image;the geometric features are defined as the coordinates of the landmark points and the semantic features are defined as the facial expression category index and face identity index of each face image. The kernel principal component analysis (KPCA)method is then applied to deal with the feature fusion task of both Gabor features and geometric features. Finally

the PLSR method is used to model the correlation between the input facial feature vectors and the semantic vectors. Based on this model

both face identity and facial expression category of any test facial image can be predicted. Experiments on both JAFFE facial expression database and AR face database show the effectiveness of the proposed method.