Abstract: There is insufficiency in expressing curve singularity for traditional Gabor wavelet transformation in face re-cognition technology that causes facial expression information hard to identify．This paper proposed a face recognition algorithm combining Gabor wavelet transform and multiple feature vectors．The algorithm firstly utilizes frequency and direction selectivity of Gabor wavelet transformation to extract the Gabor features of face multi-scale and direction and forms a joint sparse model in which the common features and expression characteristics of Gabor can be characterized in all directions and scales via calculation,at the same time,the test image feature vector can be accurately reconstructed using the two feature vector．Finally,the simulation results show that this method can effectively enhance the correct matching ratio of facial expression image and improve the recognition effect.

Key words: Face recognition,Facial expression recognition,Gabor wavelet transformation,Feature vector