Learning Modal-Invariant Angular Metric by Cyclic Projection Network for VIS-NIR Person Re-Identification
Learning Modal-Invariant Angular Metric by Cyclic Projection Network for VIS-NIR Person Re-Identification
复制标题
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
DOI：
10.1109/tip.2021.3112035
复制
发表时间：
2021
影响因子：
10.6
通讯作者：
Xiaohua Xie
中科院分区：
文献类型：
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作者：
Quan Zhang;J. Lai;Xiaohua Xie
可见光和近红外摄像机的身份识别(VIS-NIR Re-ID)有着广泛的应用。这项任务的挑战在于异质图像匹配。现有的方法试图通过复杂的特征提取策略来学习区分性特征。然而，由于模式间隙的存在，使得可见光和近红外特征的分布是不同的，这对特征度量有很大的影响，使得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的方法。我们在一个设计良好的角度空间上进行度量学习。在几何上，将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，并集中在特征与目标类别之间的角度上。具体地说，我们提出了一种循环投影网络(CPN)，它在保留身份信息的同时，将特征变换到与角度相关的空间。此外，我们还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。在现有的两个公共数据集SYSU-MM01和RegDB上的多次实验表明，该方法的性能大大超过了SOTA的性能。
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.