  In recent years, visible-spectrum face verification systems have been shown
to match the performance of experienced forensic examiners. However, such
systems are ineffective in low-light and nighttime conditions. Thermal face
imagery, which captures body heat emissions, effectively augments the visible
spectrum, capturing discriminative facial features in scenes with limited
illumination. Due to the increased cost and difficulty of obtaining diverse,
paired thermal and visible spectrum datasets, not many algorithms and
large-scale benchmarks for low-light recognition are available. This paper
presents an algorithm that achieves state-of-the-art performance on both the
ARL-VTF and TUFTS multi-spectral face datasets. Importantly, we study the
impact of face alignment, pixel-level correspondence, and identity
classification with label smoothing for multi-spectral face synthesis and
verification. We show that our proposed method is widely applicable, robust,
and highly effective. In addition, we show that the proposed method
significantly outperforms face frontalization methods on profile-to-frontal
verification. Finally, we present MILAB-VTF(B), a challenging multi-spectral
face dataset that is composed of paired thermal and visible videos. To the best
of our knowledge, with face data from 400 subjects, this dataset represents the
most extensive collection of indoor and long-range outdoor thermal-visible face
imagery. Lastly, we show that our end-to-end thermal-to-visible face
verification system provides strong performance on the MILAB-VTF(B) dataset.
