Spatio-Temporal Multi-Factor Discriminant Analysis for Individual Identification

Individual identification from skeleton sequence is a challenging task because various covariate factors may produce drastic changes in human poses, thus causing large intra-class variability. In this paper, we present a novel spatio-temporal multi-factor discriminant analysis (ST-MFDA) to reduce the impact of covariate factors on identification performance. In our approach, a set of paired factor-specific spatio-temporal projections are learned to project motion features from different factors into a common discriminant subspace. In the common subspace, features from the same individual are united and those from different individuals are separated by Fisher discriminant criterion. We show that spatio-temporal projections of various factors can be jointly learned by solving an iterative generalized eigenvalue problem. According to the experimental results from three public databases, we confirm that spatio-temporal projections trained in this way lead to significant improvements in identification accuracy under the different covariate factors.