A One-Class Classification Method for Human Gait Authentication Using Micro-Doppler Signatures

Abstract

In this letter, a radar-based gait authentication method is proposed. We focus on the overfitting problem on the target category caused by limited training data in authentication models and propose a one-class classification model to alleviate this problem. The effectiveness of such model is verified by establishing a radar-based gait dataset, which is composed of gait micro-Doppler spectrograms derived from nine human subjects. The experimental results demonstrate that, under the condition of limited training data, the performances of an authentication model degrade because misclassification of the non-target samples easily occurs. The proposed method effectively avoids this risk, performing the other existing authentication and one-class classification methods on the metric Equal Error Rate.