Robust Person Gait Identification Based on Limited Radar Measurements Using Set-Based Discriminative Subspaces Learning

Abstract

Building a robust and scalable radar-based person identification system that can accurately identify registered users and fast enroll new subjects at any time with only limited radar measurements is very desirable for real-world applications. Existing solutions, however, most resort to deep learning techniques that are heavily data-dependent and hard to scale once trained. To overcome these limitations, this article proposes a novel set-based discriminative subspaces learning (SDSL) approach and builds a high-performance gait recognition system based on it. Specifically, we model the system as a two-stage paradigm consisting of a feature embedding network for gait feature extraction, followed by an SDSL module for gait pattern modeling through low-dimensional linear subspace representing and subspaces discriminant learning. Finally, a nearest neighbor classifier (NNC) is adopted to perform person identification in the learned discriminant space by matching the subspace of a probe set with all subspaces in the gallery. Extensive experiments conducted on a radar gait dataset collected in a typical corridor scenario demonstrate not only the superiority of our model in both identification accuracy and scalability, but also its robustness against limited training data.