Gait Analysis For Human Identification Through Manifold Learning and HMM

With the increasing demands of visual surveillance systems, human identification at a distance has gained more interest. Gait is often used as an unobtrusive biometric offering the possibility to identify individuals at a distance without any interaction or co-operation with the subject. This paper presents a novel effectively method for automatic viewpoint and person identification by using only the sequence of gait silhouette. The gait silhouettes are nonlinearly transformed into low dimensional embedding and the dynamics in time-series images are modeled by HMM in the corresponding embedding space. The experimental results demonstrate that the proposed algorithm is an encouraging progress for automatic human identification.