Coupled latent least squares regression for heterogeneous face recognition

One of the most difficult challenges in automatic face recognition is computing facial similarity between two images captured in different modalities, called heterogeneous face recognition. In this paper, we propose a novel method, named as coupled latent least squares regression, to improve the heterogeneous face recognition performance. The basic assumption is that the images of one person captured in different modalities can be viewed as modality-specific transforms of a latent ideal object. We formulate this assumption in the least squares regression framework, so as to learn the coupled transforms for different modalities. In particular, the local consistency information in the each modality is considered as a constraint to improve the generalization. Extensive experiments on two cases of heterogeneous face recognition (visible light vs. near infrared, and photo vs. sketch) validate the efficiency of the proposed method.