  Visible (VIS) to near infrared (NIR) face matching is a challenging problem
due to the significant domain discrepancy between the domains and a lack of
sufficient data for training cross-modal matching algorithms. Existing
approaches attempt to tackle this problem by either synthesizing visible faces
from NIR faces, extracting domain-invariant features from these modalities, or
projecting heterogeneous data onto a common latent space for cross-modal
matching. In this paper, we take a different approach in which we make use of
the Disentangled Variational Representation (DVR) for cross-modal matching.
First, we model a face representation with an intrinsic identity information
and its within-person variations. By exploring the disentangled latent variable
space, a variational lower bound is employed to optimize the approximate
posterior for NIR and VIS representations. Second, aiming at obtaining more
compact and discriminative disentangled latent space, we impose a minimization
of the identity information for the same subject and a relaxed correlation
alignment constraint between the NIR and VIS modality variations. An
alternative optimization scheme is proposed for the disentangled variational
representation part and the heterogeneous face recognition network part. The
mutual promotion between these two parts effectively reduces the NIR and VIS
domain discrepancy and alleviates over-fitting. Extensive experiments on three
challenging NIR-VIS heterogeneous face recognition databases demonstrate that
the proposed method achieves significant improvements over the state-of-the-art
methods.
