Human Gait Recognition via Sparse Discriminant Projection Learning
As an important biometric feature, human gait has great potential in video-surveillance-based applications. In this paper, we focus on the matrix representation-based human gait recognition and propose a novel discriminant subspace learning method called sparse bilinear discriminant analysis (SBDA). SBDA extends the recently proposed matrix-representation-based discriminant analysis methods to sparse cases. By introducing the L1 and L2 norms into the objective function of SBDA, two interrelated sparse discriminant subspaces can be obtained for gait feature extraction. Since the optimization problem has no closed-form solutions, an iterative method is designed to compute the optimal sparse subspace using the L1 and L2 norms sparse regression. Theoretical analyses reveal the close relationship between SBDA and previous matrix-representation-based discriminant analysis methods. Since each nonzero element in each subspace is selected from the most important variables/factors, SBDA is potential to perform equivalent to or even better than the state-of-the-art subspace learning methods in gait recognition. Moreover, using the strategy of SBDA plus linear discriminant analysis (LDA), we can further improve the performance. A set of experiments on the standard USF HumanID and CASIA gait databases demonstrate that the proposed SBDA and SBDA + LDA can obtain competitive performance.