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Age Invariant Face Recognition On Cacdvs

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
OE-CNN	99.2%	Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition	-
AIM + CAFR	99.76%	Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition	-
LF-CNNs	98.5	Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition	-
High-Dimensional LBP	81.6	Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification	-
AIM	99.38%	Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition	-
DAL	99.4%	Decorrelated Adversarial Learning for Age-Invariant Face Recognition	-
MTLFace	99.55%	When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework	-
DeepVisage	99.13%	DeepVisage: Making face recognition simple yet with powerful generalization skills	-
MFM-CNN	97.95%	A Light CNN for Deep Face Representation with Noisy Labels	-

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Age Invariant Face Recognition On Cacdvs | SOTA | HyperAI