HMM-based gait modeling and recognition under different walking scenarios

This paper addresses gait recognition, the problem of identifying people by the way of their walk. The proposed system consists of a model-free approach which extracts features directly from the human silhouette. The dynamics of the gait are modeled using Hidden Markov Models. Experiments have been carried out on the CASIA dataset C consisting of 153 people under four walking scenarios: normal walking, slow walking, fast walking and walking while carrying a bag. The results obtained are promising and compare favorably with existing approaches.