Person re-identification across visible and near-infrared cameras (VIS-NIR Re-ID) has widespread applications. The challenge of this task lies in heterogeneous image matching. Existing methods attempt to learn discriminative features via complex feature extraction strategies. Nevertheless, the distributions of visible and near-infrared features are disparate caused by modal gap, which significantly affects feature metric and makes the performance of the existing models poor. To address this problem, we propose a novel approach from the perspective of metric learning. We conduct metric learning on a well-designed angular space. Geometrically, features are mapped from the original space to the hypersphere manifold, which eliminates the variations of feature norm and concentrates on the angle between the feature and the target category. Specifically, we propose a cyclic projection network (CPN) that transforms features into an angle-related space while identity information is preserved. Furthermore, we proposed three kinds of loss functions, AICAL, LAL and DAL, in angular space for angular metric learning. Multiple experiments on two existing public datasets, SYSU-MM01 and RegDB, show that performance of our method greatly exceeds the SOTA performance.

中文翻译：

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通过循环投影网络学习模态不变角度度量以进行 VIS-NIR 人员重新识别


可见光和近红外摄像头的人员重新识别（VIS-NIR Re-ID）具有广泛的应用。该任务的挑战在于异构图像匹配。现有方法试图通过复杂的特征提取策略来学习判别特征。然而，由于模态间隙导致可见光和近红外特征的分布不同，这显着影响了特征度量并使现有模型的性能较差。为了解决这个问题，我们从度量学习的角度提出了一种新方法。我们在精心设计的角度空间上进行度量学习。几何上，特征从原始空间映射到超球面流形，消除了特征范数的变化，集中于特征与目标类别之间的角度。具体来说，我们提出了一种循环投影网络（CPN），它将特征转换为角度相关的空间，同时保留身份信息。此外，我们在角度空间中提出了三种损失函数：AICAL、LAL 和 DAL，用于角度度量学习。对两个现有公共数据集 SYSU-MM01 和 RegDB 的多次实验表明，我们的方法的性能大大超过了 SOTA 性能。