Heterogeneous face recognition is the task of matching face images acquired from different sources (i.e., different sensors or different wavelengths) for identification or verification.
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Recent deep learning based face recognition methods have achieved great performance, but it still remains challenging to recognize very low-resolution query face like 28x28 pixels when CCTV camera is far from the captured subject.
As a consequence, massive new diverse paired heterogeneous images with the same identity can be generated from noises.
Then, in order to ensure the identity consistency of the generated paired heterogeneous images, we impose a distribution alignment in the latent space and a pairwise identity preserving in the image space.
Ranked #1 on Face Verification on CASIA NIR-VIS 2.0
Face recognition in the unconstrained environment is an ongoing research challenge.
Recent advancements in deep learning have significantly increased the capabilities of face recognition.