  Near-infrared-visible (NIR-VIS) heterogeneous face recognition matches NIR to
corresponding VIS face images. However, due to the sensing gap, NIR images
often lose some identity information so that the recognition issue is more
difficult than conventional VIS face recognition. Recently, NIR-VIS
heterogeneous face recognition has attracted considerable attention in the
computer vision community because of its convenience and adaptability in
practical applications. Various deep learning-based methods have been proposed
and substantially increased the recognition performance, but the lack of
NIR-VIS training samples leads to the difficulty of the model training process.
In this paper, we propose a new Large-Scale Multi-Pose High-Quality NIR-VIS
database LAMP-HQ containing 56,788 NIR and 16,828 VIS images of 573 subjects
with large diversities in pose, illumination, attribute, scene and accessory.
We furnish a benchmark along with the protocol for NIR-VIS face recognition via
generation on LAMP-HQ, including Pixel2Pixel, CycleGAN, and ADFL. Furthermore,
we propose a novel exemplar-based variational spectral attention network to
produce high-fidelity VIS images from NIR data. A spectral conditional
attention module is introduced to reduce the domain gap between NIR and VIS
data and then improve the performance of NIR-VIS heterogeneous face recognition
on various databases including the LAMP-HQ.
