Abstract
With the increasing demands of visual surveillance systems, human identification at a distance has recently gained more interest. Gait recognition is a process of identifying individuals by the way they walk, which 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 thesis has presents a novel effectively 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 will demonstrate that the proposed algorithm is an encouraging progress for the research of human identification.