Transductive VIS-NIR face matching

Jun-Yong Zhu, Wei-Shi Zheng, Jian-Huang Lai

Abstract: The visual-near infrared (VIS-NIR) face matching, sharing the illumination-invariant property of NIR face image and remaining the use of existing VIS face images as enrollment, has been a popular issue in recent years. However, existing techniques assume that there are sufficient pairwise VIS and NIR images for each person during training, which is not realistic in VIS-NIR matching problem, as no NIR images are available for people who have already been registered in the existing face recognition system and only a handful of pairwise VIS and NIR face images captured from new people are available. To address this problem, we formulate the VIS-NIR matching as a transductive learning problem, which is a first attempt to our best knowledge. Moreover, we propose a transductive method named Transductive Heterogeneous Face Matching (THFM) by alleviating the domains difference and learning the discriminative model for target simultaneously, making it possible to take the query/probe NIR images into account in a transductive way. Experimental results validate the effectiveness of our approach on the heterogeneous face biometric database.