Gait Recognition Using Flow Histogram Energy Image

Abstract

Human gait is of essential importance for its wide use in biometric person-identification applications. In this work, we introduce a novel spatio-temporal gait representation, Flow Histogram Energy Image (FHEI), to characterize distinctive motion information of individual gait. We first extract the Histograms of Optical Flow (HOF) descriptors of each silhouette image of gait sequence, and construct an FHEI by averaging all the HOF features of a full gait cycle. We also propose a novel approach to generate two different synthetic gait templates. Real and synthetic gait templates are then fused to enhance the recognition accuracy of FHEI. We also adopt the Non-negative Matrix Factorization (NMF) to learn a part-based representation of FHEI templates. Extensive experiments conducted on the USF HumanID gait database indicate that the proposed FHEI approach achieves superior or comparable performance in comparison with a number of competitive gait recognition algorithms.