Image-Image Translation to Enhance Near Infrared Face Recognition

With the rapid development of facial recognition, the research field of near infrared (NIR) face recognition, which is less sensitive to illumination levels, has attracted increased attention. Unfortunately, directly applying the face recognition model trained using visible light (VIS) data to NIR face data does not produce a satisfactory performance. This is due to the domain bias between the NIR images and the VIS images. To this end, we created the Outdoor NIR-VIS Face (ONVF) database and Indoor NIR Face (INF) database to increase the number of near infrared facial images for system training and evaluation. In this paper, we propose an efficient NIR face recognition method, which consists of face detection and alignment, NIR-VIS image translation and face embedding. The NIR-VIS image conversion model is capable of transforming near-infrared facial images into their corresponding VIS images whilst maintaining sufficient identity information to enable existing VIS facial recognition models to perform recognition. Extensive experiments using the INF dataset and the CSIST database have demonstrated that the proposed method yields a consistent and competitive performance for near infrared face recognition.