Abstract
mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals. This technology offers privacy protection and is resilient to weather and lighting conditions. However, its generalization performance is yet unknown and limits its practical deployment. To address this problem, in this paper, a non-synthetic dataset is collected and analyzed to reveal the presence of spatial and temporal domain shifts in mmWave gait biometric data, which significantly impacts identification accuracy. To address this issue, a novel self-aligned domain adaptation method called GaitSADA is proposed. GaitSADA improves system generalization performance by using a two-stage semi-supervised model training approach. The first stage uses semi-supervised contrastive learning and the second stage uses semi-supervised consistency training with centroid alignment. Extensive experiments show that GaitSADA outperforms representative domain adaptation methods by an average of 15.41% in low data regimes.
Abstract (translated)
毫米波雷达基于步进识别是一种新的用户身份识别方法,从毫米波雷达返回信号中捕获人类步进生物特征。该技术提供隐私保护,并对天气和照明条件具有鲁棒性。然而,其泛化性能仍未得到确定,限制了其实际应用。为了解决这一问题,本文收集和分析了一个非合成数据集,以揭示毫米波步进生物特征数据的空间和时间域变化,这些变化显著影响了身份识别准确性。为了解决这一问题,我们提出了一种名为GaitSADA的新自 align 的域适应方法。GaitSADA使用两个阶段的半监督模型训练方法来提高系统的泛化性能。第一阶段使用半监督对比度学习,第二阶段使用半监督一致性训练,并使用中心对齐。广泛的实验表明,GaitSADA在低数据状态下平均领先代表性域适应方法15.41%。
URL
https://arxiv.org/abs/2301.13384
