Manifold Adaptive Kernel Semisupervised Discriminant Analysis for Gait Recognition

Abstract

A manifold adaptive kernel semisupervised discriminant analysis algorithm for gait recognition is proposed in this paper. Motivated by the fact that the nonlinear structure captured by the data-independent kernels (such as Gaussian kernel, polynomial kernel, and Sigmoid kernel) may not be consistent with the discriminative information and the intrinsic manifold structure information of gait image, we construct two graph Laplacians by using the two nearest neighbor graphs (i.e., an intrinsic graph and a penalty graph) to model the discriminative manifold structure. We then incorporate these two graph Laplacians into the kernel deformation procedure, which leads to the discriminative manifold adaptive kernel space. Finally, the discrepancy-based semi-supervised discriminant analysis is performed in the manifold adaptive kernel space. Experimental results on the well-known USF HumanID gait database demonstrate the efficacy of our proposed algorithm.