Age-Invariant Face Recognition
5 papers with code • 4 benchmarks • 2 datasets
Age-invariant face recognition is the task of performing face recognition that is invariant to differences in age.
( Image credit: Look Across Elapse )
Benchmarks
These leaderboards are used to track progress in Age-Invariant Face Recognition
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Most implemented papers
Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition
ZhaoJ9014/High_Performance_Face_Recognition
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Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.
Decorrelated Adversarial Learning for Age-Invariant Face Recognition
neverUseThisName/Decorrelated-Adversarial-Learning
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CVPR 2019
To reduce such a discrepancy, in this paper we propose a novel algorithm to remove age-related components from features mixed with both identity and age information.
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework
We further validate MTLFace on two popular general face recognition datasets, showing competitive performance for face recognition in the wild.
ChildPredictor: A Child Face Prediction Framework with Disentangled Learning
On this basis, we formulate predictions as a mapping from parents' genetic factors to children's genetic factors, and disentangle them from external and variety factors.
Cross-Age Contrastive Learning for Age-Invariant Face Recognition
Cross-age facial images are typically challenging and expensive to collect, making noise-free age-oriented datasets relatively small compared to widely-used large-scale facial datasets.
