In this paper, we propose a novel coupled dictionary learning method, namely coupled analysis-synthesis dictionary learning, to improve the performance of person re-identification in the non-overlapping fields of different camera views. Most of the existing coupled dictionary learning methods train a coupled synthesis dictionary directly on the original feature spaces, which limits the representation ability of the dictionary. To handle the diversities of different original spaces, We first employ local Fisher discriminant analysis (LFDA) to learn a common feature space for close relationship of the same people in different views. In order to enhance the representation power of the coupled synthesis dictionary, we then learn a coupled analysis dictionary by transforming the common feature space into the coupled feature space. Experimental results on two publicly available VIPeR and CUHK01 datasets have validated the effectiveness of the proposed method.