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.

中文翻译：

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基于伪装步态的微多普勒签名人员识别的无监督域适应


近年来，基于步态的人员识别在各种应用中引起了极大的兴趣，包括安全系统和公共安全取证。同时，这项任务还面临着伪装步态的挑战。当人类受试者改变他或她穿着或携带的东西时，使用步态数据可靠地识别受试者的身份就变得具有挑战性。在本文中，我们提出了一种无监督域适应（UDA）模型，称为类感知条件下的引导子空间对齐（G-SAC），通过充分利用步态生物识别中的内在信息，根据伪装的步态数据来识别人类受试者。为了实现这一目标，我们采用邻域成分分析（NCA）来创建一个内在特征子空间，从中我们可以获得正常步态和伪装步态之间的相似性。借助所提出的自适应类感知对齐约束，可以在该子空间的指导下学习类级判别性特征表示。我们在测量的微多普勒雷达数据集上的实验结果证明了我们方法的有效性。与几种最先进方法的比较结果表明，我们的工作为相关问题提供了一种有前途的领域适应解决方案，即使在伪装模式与正常步态显着不同的情况下也是如此。此外，我们将我们的方法扩展到更复杂的多目标域适应（MTDA）挑战和基于视频的步态识别任务，优异的结果表明所提出的模型具有解决日益困难的问题的巨大潜力。