  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.
