  The gap between sensing patterns of different face modalities remains a
challenging problem in heterogeneous face recognition (HFR). This paper
proposes an adversarial discriminative feature learning framework to close the
sensing gap via adversarial learning on both raw-pixel space and compact
feature space. This framework integrates cross-spectral face hallucination and
discriminative feature learning into an end-to-end adversarial network. In the
pixel space, we make use of generative adversarial networks to perform
cross-spectral face hallucination. An elaborate two-path model is introduced to
alleviate the lack of paired images, which gives consideration to both global
structures and local textures. In the feature space, an adversarial loss and a
high-order variance discrepancy loss are employed to measure the global and
local discrepancy between two heterogeneous distributions respectively. These
two losses enhance domain-invariant feature learning and modality independent
noise removing. Experimental results on three NIR-VIS databases show that our
proposed approach outperforms state-of-the-art HFR methods, without requiring
of complex network or large-scale training dataset.
