Person identity recognition on motion capture data using multiple actions

In this paper, we introduce a novel method for person identity recognition (identification) on skeleton animation/motion capture data representing persons performing various actions. The joints positions or orientation angles and the forward differences of these quantities are used to represent a motion capture sequence. First K-means clustering is applied on training data to discover the most representative patterns on joints positions or orientation angles (dynemes) and their forward differences (F-dynemes). Each frame is then assigned to one of these patterns and the frequency of occurrence histograms for each movement are constructed in a bag-of-words fashion. Person identity recognition is done through a nearest neighbor classifier. The proposed method is experimentally tested on a number of datasets of motion capture data, with very good results.