Gait-Based Person Identification and Intruder Detection Using mm-Wave Sensing in Multi-Person Scenario

Abstract

Using mm-wave sensing to identify persons by the way they walk has recently emerged as a promising solution of biometrics, and has found versatile applications in security surveillance, automatic access control, and health monitoring. In this paper, we build a system named MGait for indoor multi-person identification and intruder detection based on gait micro-Doppler (m-D) signatures captured by a low-cost mm-wave radar. In multi-person scenario where multiple subjects concurrently share and walk within the same physical space, the proposed system continuously detects and separately tracks each subject in the range-Doppler (R-D) space frame by frame to extract their respective gait m-D signatures. Then, an open-set identification network is trained by a large-margin Gaussian mixture (L-GM) loss to learn highly discriminative feature representations and compel the learned features of the training set to follow a Gaussian mixture distribution, with each component representing a registered user. As such, the known users can be directly identified based on the class-posterior probability and the intruder can also be rejected by setting a probability threshold. The proposed system is verified on real radar measurements collected by a 77 GHz FMCW radar, achieving an overall accuracy of 88.59% in identifying up to 5 subjects, including 1 known user and 4 intruders, freely and concurrently moving in a corridor space.