NIRtoVIS Face Recognition via Embedding Relations and Coordinates of the Pairwise Features NIRtoVIS face recognition is identifying faces of two different domains by extracting domaininvariant 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 xe2x80x99Relation Modulexe2x80x99 which can simply addon 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 domaininvariant 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 domaininvariant 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 intraclass variation in training, and resulting in additional performance improvements.Different from the general face recognition models, our addon module does not need to pretrain with the large scale dataset. The proposed module finetuned only with CASIA NIRVIS 2.0 database. With the proposed module, we achieve 14.81% rank1 accuracy and 15.47% verification rate of 0.1% FAR improvements compare to two baseline models.

GRNet: An Efficient Group Ranking Network for Face Attribute Recognition Face attribute recognition methods have been far from realworld applications despite remarkable progress in recent years. Most of these methods fail to mine attribute relationships with traditional cross entropy loss and hold considerable computational complexity. In this work, we propose a group ranking network (GRNet) to investigate substantial relationships among face attributes. First, an attribute grouping manner is designed to capture interactions among spatially related attributes and enhance computational efficiency. Second, a new supervision signal is presented to model attribute ranking relationships. Under the supervision of ranking loss, GRNet learns intraattribute and interattribute ranking features to intensify the representational capability of attribute models. We evaluate our approach on aligned and unaligned CelebA datasets. Results show that the performance of the proposed approach is superior to other startoftheart methods on attribute recognition.