Heterogeneous Face Recognition
6 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.
( Image credit: Pose Agnostic Cross-spectral Hallucination via Disentangling Independent Factors )
Benchmarks
These leaderboards are used to track progress in Heterogeneous Face Recognition
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DFW
MCXFACE
Latest papers
Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation
Heterogeneous Face Recognition (HFR) aims to match face images across different domains, such as thermal and visible spectra, expanding the applicability of Face Recognition (FR) systems to challenging scenarios.
Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles
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.
DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition
BradyFU/DVG
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As a consequence, massive new diverse paired heterogeneous images with the same identity can be generated from noises.
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain Knowledge
iamgroot42/A-LINK
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IEEE Transactions on Biometrics, Behavior, and Identity Science 2020
Face recognition in the unconstrained environment is an ongoing research challenge.
A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain Knowledge
iamgroot42/A-LINK
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IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2019 2019
Recent advancements in deep learning have significantly increased the capabilities of face recognition.
Dual Variational Generation for Low-Shot Heterogeneous Face Recognition
BradyFU/DVG
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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.
