Activity-Based Person Identification Using Discriminative Sparse Projections and Orthogonal Ensemble Metric Learning

In this paper, we propose an activity-based human identification approach using discriminative sparse projections (DSP) and orthogonal ensemble metric learning (OEML). Unlike gait recognition which recognizes person only from his/her walking activity, this study aims to identify people from more general types of human activities such as eating, drinking, running, and so on. That is because people may not always walk in the scene and gait recognition fails to work in this scenario. Given an activity video, human body mask in each frame is first extracted by background substraction. Then, we propose a DSP method to map these body masks into a low-dimensional subspace and cluster them into a number of clusters to form a dictionary, simultaneously. Subsequently, each video clip is pooled as a histogram feature for activity representation. Lastly, we propose an OEML method to learn a similarity distance metric to exploit discriminative information for recognition. Experimental results show the effectiveness of our proposed approach and better recognition rate is achieved than state-of-the-art methods.