In recent years, gait-based person identification has gained significant interest for a variety of applications, including security systems and public security forensics. Meanwhile, this task is faced with the challenge of disguised gaits. When a human subject changes what he or she is wearing or carrying, it becomes challenging to reliably identify the subject's identity using gait data. In this paper, we propose an unsupervised domain adaptation (UDA) model, named Guided Subspace Alignment under the Class-Aware condition (G-SAC), to recognize human subjects based on their disguised gait data by fully exploiting the intrinsic information in gait biometrics. To accomplish this, we employ neighbourhood component analysis (NCA) to create an intrinsic feature subspace from which we can obtain similarities between normal and disguised gaits. With the aid of a proposed constraint for adaptive class-Aware alignment, the class-level discriminative feature representation can be learned guided by this subspace. Our experimental results on a measured micro-Doppler radar dataset demonstrate the effectiveness of our approach. The comparison results with several state-of-The-Art methods indicate that our work provides a promising domain adaptation solution for the concerned problem, even in cases where the disguised pattern differs significantly from the normal gaits. Additionally, we extend our approach to more complex multi-Target domain adaptation (MTDA) challenge and video-based gait recognition tasks, the superior results demonstrate that the proposed model has a great deal of potential for tackling increasingly difficult problems.

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.

With the substantial increase of the FMCW radars used for autonomous driving and other applications in the area of surveillance, mutual interference has become a major concern. Recently, Deep Learning (DL) models have been used in FMCW radar interference mitigation with great success, but no research has been conducted in processing the time-frequency (t-f) maps of acquired beat signals. Considering the different distributions of useful beat signals and interferences in the t-f domain, a fully convolutional network (FCN) is proposed to suppress the interference and noise in the t-f spectrum obtained by the short-time Fourier transform (STFT) algorithm. The experimental results on the simulated radar signals show that the proposed FCN provides superior interference suppression with few parameters. Moreover, the qualitative results on the measured radar signals collected in real-world scenarios emphasize the excellent generalization capacity of the model. Finally, we show that our proposed approach achieves the best performance compared to state-of-the-art techniques.