  Near infrared (NIR) to Visible (VIS) face matching is challenging due to the
significant domain gaps as well as a lack of sufficient data for cross-modality
model training. To overcome this problem, we propose a novel method for paired
NIR-VIS facial image generation. Specifically, we reconstruct 3D face shape and
reflectance from a large 2D facial dataset and introduce a novel method of
transforming the VIS reflectance to NIR reflectance. We then use a
physically-based renderer to generate a vast, high-resolution and
photorealistic dataset consisting of various poses and identities in the NIR
and VIS spectra. Moreover, to facilitate the identity feature learning, we
propose an IDentity-based Maximum Mean Discrepancy (ID-MMD) loss, which not
only reduces the modality gap between NIR and VIS images at the domain level
but encourages the network to focus on the identity features instead of facial
details, such as poses and accessories. Extensive experiments conducted on four
challenging NIR-VIS face recognition benchmarks demonstrate that the proposed
method can achieve comparable performance with the state-of-the-art (SOTA)
methods without requiring any existing NIR-VIS face recognition datasets. With
slightly fine-tuning on the target NIR-VIS face recognition datasets, our
method can significantly surpass the SOTA performance. Code and pretrained
models are released under the insightface
(https://github.com/deepinsight/insightface/tree/master/recognition).
