Cluster-Pairwise Discriminant Analysis

Abstract

Pattern recognition problems often suffer from the larger intra-class variation due to situation variations such as pose, walking speed, and clothing variations in gait recognition. This paper describes a method of discriminant subspace analysis focused on situation cluster pair. In training phase, both a situation cluster discriminant subspace and class discriminant subspaces for the situation cluster pair by using training samples of non recognition-target classes. In testing phase, given a matching pair of patterns of recognition-target classes, posterior of situation cluster pairs is estimated at first, and then the distance is calculated in the corresponding cluster-pairwise class discriminant subspace. The experiments both with simulation data and real data show the effectiveness of the proposed method.