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.
( Image credit: Pose Agnostic Cross-spectral Hallucination via Disentangling Independent Factors )
Benchmarks
These leaderboards are used to track progress in Heterogeneous Face Recognition
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
DFW
MCXFACE
Most implemented papers
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.
Dual Variational Generation for Low-Shot Heterogeneous Face Recognition
BradyFU/DVG
•
•
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.
A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain Knowledge
iamgroot42/A-LINK
•
•
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.
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain Knowledge
iamgroot42/A-LINK
•
•
IEEE Transactions on Biometrics, Behavior, and Identity Science 2020
Face recognition in the unconstrained environment is an ongoing research challenge.
DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition
BradyFU/DVG
•
•
As a consequence, massive new diverse paired heterogeneous images with the same identity can be generated from noises.
