Abstract: With the wide application of visible-infrared dual-mode cameras in video surveillance, cross-modal face recognition has become a research hotspot in the field of computer vision. The translation of NIR domain face images into VIS domain face images is a key problem in cross-modal face recognition, which has important research value in the fields of criminal investigation and security. Aiming at the problems that facial contours are easily distorted and skin color restoration is unrealistic during the coloring process of NIR face images, this paper proposes a NIR-VIS face images translation method under a dual contrastive learning framework. This method constructs a generator network based on the StyleGAN2 structure and embeds it into the dual contrastive learning framework to exploit the fine-grained characteristics of face images using bidirectional contrastive learning. Meanwhile, a facial edge enhancement loss is designed to further enhance the facial details in the generated face images and improve the visual effects of the face images using the facial edge information extracted from the source domain images. Finally, experiments on the NIR-VIS Sx1 and NIR-VIS Sx2 datasets show that, compared with the recent mainstream methods, the VIS face images generated by this method are closer to the real images and possesses more facial edge details and skin color information of the face images.