Graph-based sparse coding and embedding for activity-based human identification
In this paper, we propose a new graph-based sparse coding and embedding (GSCE) method for activity-based human identification. Different from human activity recognition which recognizes different types of human activities such as walking, running, eating, and drinking, in this study, we aim to identify persons from his/her activities. To our best knowledge, this problem has been seldom investigated in the literature. Given a training set of video clips, we first extract human body mask in each frame and learn a codebook to quantize these masks into a histogram feature by using a graph-based sparse coding technique to better preserve the similarity information of different frames within a same video clip. Moreover, we also learn a mapping to project each frame into a low-dimensional subspace to speed up the quantization procedure, such that more discriminative information can be further exploited for classification. Experimental results on three databases are presented to show the efficacy of the proposed method.