It is a challenge for person identification to find an appropriate person feature representation method which can reduce intra-personal variations and enlarge inter-personal differences. A deep network for person identification based on joint identification-verification was proposed to solve this problem. First, the deep network model for identification was used to enlarge the inter-personal differences of different people while the verification model was used for reducing the intra-personal distance of the same person. Second, the discriminative feature vectors were extracted by sharing parameters and jointing deep networks of identification and verification. At last, the joint Bayesian algorithm was adopted to calculate the similarity of two persons, which improved the accuracy of pedestrian alignment. Experimental results prove that the proposed method has higher pedestrian recognition accuracy compared with some other state-of-art methods on VIPeR database; meanwhile, the joint identification-verification deep network has higher convergence speed and recognition accuracy than those of separated deep networks.