In order to solve the problem of insuf ficient featu re extraction and sensitive to the noise of tranditional face recognition methods,a new face recotnition algorithm based on significance loca l directional pattern (SLDP) and deep learning (DL) is proposed in this paper.First,a new face feature extraction method based on SLDP is given,the local # shape neighborhood of each pixel is equally divided into 3×3sub-neigh borhoods,then eight edge gradient values of each sub-neighborhood are obtained by convolving it with eight Kirsch masks respectively,and SLDP just utilizes the directions of the most prominent edge response value and the second most prominent one.The SLDPs are generated by statistical analysis according to the occurrence rates of the SLDP codes in a ma ss of facial images.Finally,the face descriptor is represented by using the global concatenated histogram based on SLDP extracte d from the face image which is divided into several sub-regions.Second,making SLDP texture features as the input of d eep belief net (DBN),and greedy training for the network layer is made layer by layer to obtain good network parameters.At last,t he trained net is used to predict the test samples labels.The simulation results show that the algorithm can obtain higher recognition rate than traditional algorithms.