Heterogeneous Face Recognition
5 papers with code • 3 benchmarks • 2 datasets
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.
The core idea of the proposed approach is to add a novel neural network block called Prepended Domain Transformer (PDT) in front of a pre-trained face recognition (FR) model to address the domain gap.
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.
Recent advancements in deep learning have significantly increased the capabilities of face recognition.
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain Knowledge
Face recognition in the unconstrained environment is an ongoing research challenge.
As a consequence, massive new diverse paired heterogeneous images with the same identity can be generated from noises.