NIR-VIS Heterogeneous Face Synthesis via Enhanced Asymmetric CycleGAN

Abstract

Heterogeneous face image involves many fields, among which the near infrared (NIR) and visible (VIS) face image recognition is a hot field. NIR image has excellent imaging effect under extreme illumination conditions. Although few NIR images are available, unpaired image-to-image translation provides a solution for converting NIR-VIS to each other. However, NIR image contains relatively poor information, while VIS image contains relatively rich information, which results in asymmetry between NIR and VIS, and not suitable for unpaired translation based on two domains of the same complexity. In this paper, an enhanced asymmetric CycleGAN(EN-ASGAN) with edge retention module, auxiliary encoder module and generators equipped with different sizes is used to convert NIR-VIS face images. To validate the effective of EN-ASGAN, we conduct experiments on CASIA NIR-VIS 2.0 dataset. The experimental results are evaluated qualitatively and quantitatively and show that EN-ASGAN is effective for heterogeneous face generation in unpaired NIR-VIS images.