  In the face recognition application scenario, we need to process facial
images captured in various conditions, such as at night by near-infrared (NIR)
surveillance cameras. The illumination difference between NIR and visible-light
(VIS) causes a domain gap between facial images, and the variations in pose and
emotion also make facial matching more difficult. Heterogeneous face
recognition (HFR) has difficulties in domain discrepancy, and many studies have
focused on extracting domain-invariant features, such as facial part relational
information. However, when pose variation occurs, the facial component position
changes, and a different part relation is extracted. In this paper, we propose
a part relation attention module that crops facial parts obtained through a
semantic mask and performs relational modeling using each of these
representative features. Furthermore, we suggest component adaptive triplet
loss function using adaptive weights for each part to reduce the intra-class
identity regardless of the domain as well as pose. Finally, our method exhibits
a performance improvement in the CASIA NIR-VIS 2.0 and achieves superior result
in the BUAA-VisNir with large pose and emotion variations.
