Gait Analysis for Human Identification through Manifold Learning and HMM

Gait recognition is a process of identifying individuals by the way they walk. Gait is often used as a unobstrusive biometric offering the possibility to identify people at a distance without any interaction or co-operation with the subject. This paper presents a novel method for both automatic viewpoint and person identification using only the silhouette sequence of gait. 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 the gait analysis research.