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Face Recognition On Lfw

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
DCQ0.998Dynamic Class Queue for Large Scale Face Recognition In the Wild-
EdgeFace - S (g=0.5)0.9978EdgeFace: Efficient Face Recognition Model for Edge Devices-
AdaFace + WebFace4M + R1000.9980AdaFace: Quality Adaptive Margin for Face Recognition-
PIC - QMagFace-PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric (Face) Recognition-
FaceTransformer+OctupletLoss0.9973Octuplet Loss: Make Face Recognition Robust to Image Resolution-
PIC - ArcFace-PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric (Face) Recognition-
QMagFace0.9850QMagFace: Simple and Accurate Quality-Aware Face Recognition-
CircleLoss0.9973Circle Loss: A Unified Perspective of Pair Similarity Optimization-
EdgeFace - XS (g=0.6)0.9973EdgeFace: Efficient Face Recognition Model for Edge Devices-
OcularAI-Face0.945MagicEye: An Intelligent Wearable Towards Independent Living of Visually Impaired-
PIC - MagFace-PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric (Face) Recognition-
ArcFace + MS1MV2 + R1000.9983AdaFace: Quality Adaptive Margin for Face Recognition-
SymFace + AdaFace + ResNet100 +WebFace (MS1MV2)0.9985SymFace: Additional Facial Symmetry Loss for Deep Face Recognition-
Prodpoly0.99833Deep Polynomial Neural Networks-
GhostFaceNetV2-1 (MS1MV3)0.998667GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations
DiscFace0.9983DiscFace: Minimum Discrepancy Learning for Deep Face Recognition-
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Face Recognition On Lfw | SOTA | HyperAI