Face synthesis from near-infrared to visual light spectrum using quotient image and kernel-based multifactor analysis

This paper addresses the problem of synthesizing an artificial visual light (VIS) facial image from near-infrared (NIR) input. After extensively assessing photic characteristics of tissues at human skin surface, we propose a framework for this task. Firstly, we take the quotient images for training and reconstruction, so that information related to face structure can be preserved. Secondly, to handle heterogeneous blur resulted from multiple scattering within tissues, we introduce kernel-based strategy as a powerful nonlinear analyzing instrument. Finally, as in our application the image ensembles involve multiple factors, a tensor structure is employed to transform heterogeneous face data into uniform subspaces. Comparative results show that our synthesized images are both suited for human vision and discriminative for machine recognition.