Gait Recognition in the Presence of Occlusion: A New Dataset and Baseline Algorithms

Abstract

Human gait is an important biometric feature for identification of people. In this paper we present a new dataset for gait recognition. The presented database overcomes a crucial limitation of other state-of-the-art gait recognition databases. More specifically this database addresses the problem of dynamic and static inter object occlusion. Furthermore this dataset offers three new kinds of gait variations, which allow for challenging evaluation of recognition algorithms. In addition to presenting the database we present two baseline algorithms (Color histograms, Gait Energy Image) to perform person identification using gait. These algorithms already show promising results on the presented database.