Activity-based person identification using sparse coding and discriminative metric learning

This paper presents a new activity-based person identification method using sparse coding and discriminative metric learning. Different from gait recognition where human walking activity is only utilized for person identification, we aim to recognize people from different activities such as running, jumping, skipping, and so on. For each activity video clip, we extract the binary human body mask using background substraction. Then, we cluster these body masks into a number of clusters by sparse coding with mean pooling to extract features for each video clip. Subsequently, we learn a discriminative distance metric under which intraclass (activities performed by the same person) variations are minimized and the interclass (activities performed by different persons) are maximized, simultaneously, such that more discriminative information can be exploited for recognition. Experimental results on a publicly available database are presented to show the efficacy of our proposed method.