A method for face recognition based on the Gabor feature and deep belief network（ DBN） is proposed.By extracting different scales image of Gabor face images for convolution fusion and fused feature image is used as input data of DBN. Many layers are trained in order to get more abstract representation. In whole training process

cross entropy method is adopted to fine-tune DBN. The Softmax regression classifier is used for classification which is implemented at the top layer. The experimental result in AR face database shows that when Gabor feature extract combining with DBN are applied to face recognition

its accuracy reaches 92. 7 %. Comparing with other shallowlayer learning models

DBN not only studies the high-level features of the data

but also reduces dimension and improves the precision of the classifier

which finally improves face recognition rate.