Robust gait recognition via discriminative set matching

In this paper, we propose a framework for gait recognition across varying views and walking conditions based on gait sequences collected from multiple viewpoints. Different from most existing view-dependent gait recognition systems, we devise a new Multiview Subspace Representation (MSR) method which considers gait sequences collected from different views of the same subject as a feature set and extracts a linear subspace to describe the feature set. Subspace-based feature representation methods measure the variances among samples, and can handle certain intra-subject variations. To better exploit the discriminative information from these subspaces for recognition, we further propose a marginal canonical correlation analysis (MCCA) method which maximizes the margins of interclass subspaces within a neighborhood. Experimental results on a widely used multiview gait database are presented to demonstrate the effectiveness of the proposed framework.

► We propose a framework for multiview gait recognition across varying views and walking conditions.
► Our approach is computationally inexpensive and suitable for real applications.
► Our method can perform robust even with limited number of training samples of each subject.
► Extensive experimental results are presented to demonstrate the effectiveness of the proposed framework.