论文标题
dblface：基于域的基于域的标签，用于NIR-VIS异质识别
DBLFace: Domain-Based Labels for NIR-VIS Heterogeneous Face Recognition
论文作者
论文摘要
基于深度学习的域不变特征学习方法正在以近红外和可见的（NIR-VIS）异质性识别前进。但是，由于较大的类内变异和缺乏用于训练的NIR图像，这些方法很容易过度拟合。在本文中，我们介绍了基于域的标签面（DBLFACE），这是一种基于以下假设：主体不是由单个标签代表的，而是由一组标签表示。每个标签代表特定域的图像。特别是，每个受试者一组两个标签，一个用于NIR图像，另一个用于VIS图像，用于训练NIR-VIS面部识别模型。将图像分类为不同领域可减少类内变异，并减少训练中数据不平衡的负面影响。为了训练一个具有一组标签的网络，我们引入了基于域的角缘损失和最大角度损耗，以维持阶层间差异，并在集合中执行标签的密切关系。定量实验证实，dblface在Edge20数据集上显着提高了排名1识别率的6.7％，并在Casia Nir-Vis 2.0数据集上实现了最先进的性能。
Deep learning-based domain-invariant feature learning methods are advancing in near-infrared and visible (NIR-VIS) heterogeneous face recognition. However, these methods are prone to overfitting due to the large intra-class variation and the lack of NIR images for training. In this paper, we introduce Domain-Based Label Face (DBLFace), a learning approach based on the assumption that a subject is not represented by a single label but by a set of labels. Each label represents images of a specific domain. In particular, a set of two labels per subject, one for the NIR images and one for the VIS images, are used for training a NIR-VIS face recognition model. The classification of images into different domains reduces the intra-class variation and lessens the negative impact of data imbalance in training. To train a network with sets of labels, we introduce a domain-based angular margin loss and a maximum angular loss to maintain the inter-class discrepancy and to enforce the close relationship of labels in a set. Quantitative experiments confirm that DBLFace significantly improves the rank-1 identification rate by 6.7% on the EDGE20 dataset and achieves state-of-the-art performance on the CASIA NIR-VIS 2.0 dataset.
