Speed Invariant, Human Gait Based Recognition System for Video Surveillance Security

Human gait provides an important and useful behavioral biometric signature which characterizes the nature of an individual’s walking pattern. This inherent knowledge of gait feature confirms the correct identification of a person in a video surveillance footage scenario. In this paper, we attempt to use computer vision based technique to derive the gait signature of a person which is a major criterion for the gait based recognition system. The gait signature has been obtained from the sequence of silhouette images at various gait speeds varying from 2km/hr. to 7km/hr. The OU- ISIR Treadmill walking speed databases have been used in our research work. The joint angles of knee and ankle are computed from the stick figure of corresponding human silhouettes which lead to construct our feature template together with the other gait attributes such as width, height, area and diagonal angle of human silhouette. The combined gait features will make the system robust in different gait speeds. The major concept behind making the gait recognition speed invariant is that the human can walk in finite speed so instead of training the classifier for a single speed the classifier is to be trained for multiple speeds. A minimum distance classifier is used to separate out different cluster of subject with combined feature vectors at different gait speeds.