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 NIR-VIS 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 \(\mathbf{L} {} \mathbf{a} \)rge-Scale \(\mathbf{M} \)ulti-\(\mathbf{P} \)ose \(\mathbf{H} \)igh-\(\mathbf{Q} \)uality NIR-VIS database ‘\(\mathbf{LAMP}-HQ \)’ 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 Pixel2-Pixel, CycleGAN, ADFL, PCFH, and PACH. 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.

近红外可见（NIR-VIS）异构面部识别将NIR与相应的VIS面部图像匹配。然而，由于感应间隙，NIR图像通常会丢失一些身份信息，因此NIR-VIS识别问题比传统的VIS人脸识别更加困难。近来，NIR-VIS异质人脸识别由于其在实际应用中的便利性和适应性而在计算机视觉界引起了相当大的关注。已经提出了各种基于深度学习的方法，并大大提高了识别性能，但是缺少NIR-VIS训练样本导致模型训练过程的困难。在本文中，我们提出了一个新的\（\ mathbf {L} {} \ mathbf {a} \） rge-Scale \（\ mathbf {M} \） ulti-\（\ mathbf {P} \） ose \（\ mathbf {H} \）高- \（\ mathbf {Q} \）实体NIR-VIS数据库' \（\ mathbf {LAMP} -HQ \）包含573个对象的56,788 NIR和16,828 VIS图像，这些对象在姿势，照明，属性，场景和附件方面差异很大。我们通过在LAMP-HQ上生成NIR-VIS人脸识别协议，提供了一个基准，包括Pixel2-Pixel，CycleGAN，ADFL，PCFH和PACH。此外，我们提出了一种新颖的基于示例的变分频谱注意力网络，以从NIR数据中生成高保真VIS图像。引入了频谱条件关注模块，以减少NIR和VIS数据之间的域间隙，然后在包括LAMP-HQ在内的各种数据库上提高NIR-VIS异类人脸识别的性能。