Gait-based recognition of humans using continuous HMMs

Gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. In this paper, we propose a view-based approach to recognize humans through gait. The width of the outer contour of the binarized silhouette of a walking person is chosen as the image feature. A set of stances or key frames that occur during the walk cycle of an individual is chosen. Euclidean distances of a given image from this stance set are computed and a lower-dimensional observation vector is generated. A continuous hidden Markov model (HMM) is trained using several such lower-dimensional vector sequences extracted from the video. This methodology serves to compactly capture structural and transitional features that are unique to an individual. The statistical nature of the HMM renders overall robustness to gait representation and recognition. The human identification performance of the proposed scheme is found to be quite good when tested in natural walking conditions.