Multi-view Multi-modal Gait Based Human Identity Recognition from 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, the importance of infrared and visible range images in ascertaining identity, the impact of multimodal fusion, efficient subspace features and classifier methods, and the 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 (NB/MLP/SVM/SMO) on different datasets from a publicly available gait database CASIA, show significant improvement in recognition accuracies with multimodal fusion of multi-view gait images from visible and infrared cameras acquired from video surveillance scenarios.