论文标题
通过嵌入关系和成对特征的嵌入关系和坐标
NIR-to-VIS Face Recognition via Embedding Relations and Coordinates of the Pairwise Features
论文作者
论文摘要
NIR到VIS的面部识别是通过提取域不变特征来识别两个不同域的面。但是，由于两个不同的领域特征以及缺乏NIR FACE数据集，这是一个具有挑战性的问题。为了在使用现有面部识别模型时减少域差异，我们提出了一个“关系模块”，它可以简单地添加到任何面部识别模型中。从面部图像中提取的本地特征包含面部每个组件的信息。基于两个不同的域特征，使用本地特征之间的关系比以原样的范围更不变。除了这些关系外，位置信息（例如从嘴唇到下巴到眼睛到眼睛到眼睛的距离）也提供了域不变的信息。在我们的关系模块中，关系层隐含地捕获关系，并协调层对位置信息进行建模。此外，我们提出的三重态损失和有条件的边缘损失减少了训练中类内部的变化，并导致了进一步的改进。与一般面部识别模型不同，我们的附加模块不需要与大型数据集进行预训练。所提出的模块仅使用Casia Nir-Vis 2.0数据库进行了微调。通过提出的模块，我们达到了14.81％的排名1精度和15.47％的验证率为0.1％的远距离改进，与两个基线模型相比。
NIR-to-VIS face recognition is identifying faces of two different domains by extracting domain-invariant features. However, this is a challenging problem due to the two different domain characteristics, and the lack of NIR face dataset. In order to reduce domain discrepancy while using the existing face recognition models, we propose a 'Relation Module' which can simply add-on to any face recognition models. The local features extracted from face image contain information of each component of the face. Based on two different domain characteristics, to use the relationships between local features is more domain-invariant than to use it as it is. In addition to these relationships, positional information such as distance from lips to chin or eye to eye, also provides domain-invariant information. In our Relation Module, Relation Layer implicitly captures relationships, and Coordinates Layer models the positional information. Also, our proposed Triplet loss with conditional margin reduces intra-class variation in training, and resulting in additional performance improvements. Different from the general face recognition models, our add-on module does not need to pre-train with the large scale dataset. The proposed module fine-tuned only with CASIA NIR-VIS 2.0 database. With the proposed module, we achieve 14.81% rank-1 accuracy and 15.47% verification rate of 0.1% FAR improvements compare to two baseline models.
