  Heterogeneous Face Recognition (HFR) is a task that matches faces across two
different domains such as visible light (VIS), near-infrared (NIR), or the
sketch domain. Due to the lack of databases, HFR methods usually exploit the
pre-trained features on a large-scale visual database that contain general
facial information. However, these pre-trained features cause performance
degradation due to the texture discrepancy with the visual domain. With this
motivation, we propose a graph-structured module called Relational Graph Module
(RGM) that extracts global relational information in addition to general facial
features. Because each identity's relational information between intra-facial
parts is similar in any modality, the modeling relationship between features
can help cross-domain matching. Through the RGM, relation propagation
diminishes texture dependency without losing its advantages from the
pre-trained features. Furthermore, the RGM captures global facial geometrics
from locally correlated convolutional features to identify long-range
relationships. In addition, we propose a Node Attention Unit (NAU) that
performs node-wise recalibration to concentrate on the more informative nodes
arising from relation-based propagation. Furthermore, we suggest a novel
conditional-margin loss function (C-softmax) for the efficient projection
learning of the embedding vector in HFR. The proposed method outperforms other
state-of-the-art methods on five HFR databases. Furthermore, we demonstrate
performance improvement on three backbones because our module can be plugged
into any pre-trained face recognition backbone to overcome the limitations of a
small HFR database.
