The importance of person identification techniques is increasing for visual surveillance applications. In social living scenarios, people often act in groups composed of friends, family, and co-workers, and this is a useful cue for person identification. This paper describes a method for person identification in video sequences based on this group cue. In the proposed approach, the relationships between the people in an input sequence are modeled using a graphical model. The identity of each person is then propagated to their neighbors in the form of message passing in a graph via belief propagation, depending on each person's group affiliation information and their characteristics, such as spatial distance and velocity vector difference, so that the members of the same group with similar characteristics enhance each other's identities as group members. The proposed method is evaluated through gait-based person identification experiments using both simulated and real input sequences. Experimental results show that the identification performance is considerably improved when compared with that of the straightforward method based on the gait feature alone.