Semantic Attribute Classification Related to Gait

Abstract

Human gait, as a behavioral biometric, has recently gained significant attention from computer vision researchers. But there are some challenges which hamper using this biometric in real applications. Among these challenges is clothing variations and carrying objects which influence on its accuracy. In this paper, we propose a semantic classification based method in order to deal with such challenges. Different predictive models are elaborated in order to determine the most relevant model for this task. Experimental results on CASIA-B gait database show the performance of our proposed method.