Multiscenario Open-Set Gait Recognition Based on Radar Micro-Doppler Signatures

Abstract

Unlike face recognition, gait recognition does not require the cooperation of human targets, making it an attractive research area in both academia and industry. While there has been extensive research in recent years on the feasibility of employing radar micro-Doppler signatures for gait recognition, little research has been conducted on how to deal with unknown target detection in micro-Doppler-based gait recognition problems, referred to as ’open-set’ gait recognition. In this case, it is probable that the unknown samples will resemble known samples, hence increasing the likelihood of misclassification. In this paper, we focus on the problem of multi-scenario open-set gait recognition and present a new open-set classifier that minimizes misclassification in open-set recognition. We create a compact representation space to reduce false negatives with a supervised contrastive learning method and propose an ensemble-based out-of-distribution (OOD) detection module so that false positives can be alleviated at both the pixel and semantic levels. We perform the radar measurements in eight different walking manners and construct a micro-Doppler-based multi-scenario gait dataset for further evaluate the proposed method’s performance. The results reveal that the proposed method outperforms the previous state-of-the-art open-set models, demonstrating its great potential for solving the radar-based multi-scenario open-set gait recognition problem. This research would facilitate gait recognition in complex scenarios and make non-contact sensing applicable to industrial security.