  VIS-NIR face recognition remains a challenging task due to the distinction
between spectral components of two modalities and insufficient paired training
data. Inspired by the CycleGAN, this paper presents a method aiming to
translate VIS face images into fake NIR images whose distributions are intended
to approximate those of true NIR images, which is achieved by proposing a new
facial feature embedded CycleGAN. Firstly, to learn the particular feature of
NIR domain while preserving common facial representation between VIS and NIR
domains, we employ a general facial feature extractor (FFE) to replace the
encoder in the original generator of CycleGAN. For implementing the facial
feature extractor, herein the MobileFaceNet is pretrained on a VIS face
database, and is able to extract effective features. Secondly, the
domain-invariant feature learning is enhanced by considering a new pixel
consistency loss. Lastly, we establish a new WHU VIS-NIR database which varies
in face rotation and expressions to enrich the training data. Experimental
results on the Oulu-CASIA NIR-VIS database and the WHU VIS-NIR database show
that the proposed FFE-based CycleGAN (FFE-CycleGAN) outperforms
state-of-the-art VIS-NIR face recognition methods and achieves 96.5\% accuracy.
