A multi-modal gait based human identity recognition system based on surveillance videos

In this paper we propose a novel human-identification scheme from long range gait profiles in surveillance videos. We investigate the role of multi view gait images acquired from multiple cameras, importance of infrared and visible range images in ascertaining identity, and role of soft/secondary biometric (walking style) in enhancing the accuracy and robustness of the identification systems, Experimental evaluation of several subspace based gait feature extraction approaches (PCA/LDA) and learning classifier methods (MLP/SMO) on different datasets from a publicly available gait database CASIA, show that it is possible to do large scale human identity recognition from gait information captured in multiple view-points, with multiple cameras and with usage of subtle soft/secondary biometric information.