Age Invariant Face Recognition On Cacdvs

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Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

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

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Age Invariant Face Recognition On Cacdvs | SOTA | HyperAI