  Cross-spectral face recognition (CFR) refers to recognizing individuals using
face images stemming from different spectral bands, such as infrared vs.
visible. While CFR is inherently more challenging than classical face
recognition due to significant variation in facial appearance caused by the
modality gap, it is useful in many scenarios including night-vision biometrics
and detecting presentation attacks. Recent advances in convolutional neural
networks (CNNs) have resulted in significant improvement in the performance of
CFR systems. Given these developments, the contributions of this survey are
three-fold. First, we provide an overview of CFR, by formalizing the CFR
problem and presenting related applications. Secondly, we discuss the
appropriate spectral bands for face recognition and discuss recent CFR methods,
placing emphasis on deep neural networks. In particular we describe techniques
that have been proposed to extract and compare heterogeneous features emerging
from different spectral bands. We also discuss the datasets that have been used
for evaluating CFR methods. Finally, we discuss the challenges and future lines
of research on this topic.
