Gait recognition by learning distributed key poses

Gait recognition is receiving increasing attention from computer vision researchers for its applicability in areas such as visual surveillance, access control, or smart interfaces. Most existing research attempts to model individual gait patterns as sequences of temporal templates either by determining gait cycles or by aggregating spatio-temporal information into a 2D signature. This paper presents a simple yet efficient and effective approach to gait recognition based on a contour-distance feature and key pose learning. Unlike existing work, gait patterns are modelled as a non-temporal collection of key poses distributed over gait cycles. Experimental results on a large multi-view benchmark data set exhibit high recognition accuracy and robustness against changes in viewpoint. Consequently, this paper establishes that non-temporal methods can accomplish efficient and accurate gait recognition.