task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Facial Recognition and Modelling > Face Recognition | IJB-B | ArcFace + MS1MV2 + R100 | https://arxiv.org/abs/2204.00964v2 | Rank-1 | 0.9450 |
Facial Recognition and Modelling > Face Recognition | IJB-B | ElasticFace-Cos | https://arxiv.org/abs/2109.09416v4 | TAR @ FAR=0.0001 | 0.953 |
Facial Recognition and Modelling > Face Recognition | IJB-B | AdaFace + MS1MV3 + R100 | https://arxiv.org/abs/2204.00964v2 | TAR @ FAR=0.0001 | 0.9425 |
Facial Recognition and Modelling > Face Recognition | MFR | Partial FC | https://arxiv.org/abs/2203.15565v1 | MFR-ALL | 97.85 |
Facial Recognition and Modelling > Face Recognition | MFR | Partial FC | https://arxiv.org/abs/2203.15565v1 | MFR-MASK | 90.88 |
Facial Recognition and Modelling > Face Recognition | MFR | Partial FC | https://arxiv.org/abs/2203.15565v1 | African | 98.07 |
Facial Recognition and Modelling > Face Recognition | MFR | Partial FC | https://arxiv.org/abs/2203.15565v1 | Caucasian | 98.81 |
Facial Recognition and Modelling > Face Recognition | MFR | Partial FC | https://arxiv.org/abs/2203.15565v1 | South Asian | 98.66 |
Facial Recognition and Modelling > Face Recognition | MFR | Partial FC | https://arxiv.org/abs/2203.15565v1 | East Asian | 89.97 |
Facial Recognition and Modelling > Face Recognition | AgeDB-30 | Prodpoly | https://arxiv.org/abs/2006.13026v2 | Accuracy | 0.98467 |
Facial Recognition and Modelling > Face Recognition | AgeDB-30 | ElasticFace-Cos | https://arxiv.org/abs/2109.09416v4 | Accuracy | 0.9835 |
Facial Recognition and Modelling > Face Recognition | AgeDB-30 | Transformer loss+ArcFace ResNet100 | https://arxiv.org/abs/2412.02198v2 | Accuracy | 0.9831 |
Facial Recognition and Modelling > Face Recognition | AgeDB-30 | DCQ | https://arxiv.org/abs/2105.11113v1 | Accuracy | 0.9823 |
Facial Recognition and Modelling > Face Recognition | LFW | GhostFaceNetV2-1 (MS1MV3) | https://ieeexplore.ieee.org/document/10098610 | Accuracy | 0.998667 |
Facial Recognition and Modelling > Face Recognition | LFW | SymFace + AdaFace + ResNet100 +WebFace (MS1MV2) | https://arxiv.org/abs/2409.11816v1 | Accuracy | 0.9985 |
Facial Recognition and Modelling > Face Recognition | LFW | Prodpoly | https://arxiv.org/abs/2006.13026v2 | Accuracy | 0.99833 |
Facial Recognition and Modelling > Face Recognition | LFW | DiscFace | https://openaccess.thecvf.com/content/ACCV2020/html/Kim_DiscFace_Minimum_Discrepancy_Learning_for_Deep_Face_Recognition_ACCV_2020_paper.html | Accuracy | 0.9983 |
Facial Recognition and Modelling > Face Recognition | LFW | ArcFace + MS1MV2 + R100 | https://arxiv.org/abs/2204.00964v2 | Accuracy | 0.9983 |
Facial Recognition and Modelling > Face Recognition | LFW | DCQ | https://arxiv.org/abs/2105.11113v1 | Accuracy | 0.998 |
Facial Recognition and Modelling > Face Recognition | LFW | AdaFace + WebFace4M + R100 | https://arxiv.org/abs/2204.00964v2 | Accuracy | 0.9980 |
Facial Recognition and Modelling > Face Recognition | LFW | EdgeFace - S (g=0.5) | https://arxiv.org/abs/2307.01838v2 | Accuracy | 0.9978 |
Facial Recognition and Modelling > Face Recognition | LFW | CircleLoss | https://arxiv.org/abs/2002.10857v2 | Accuracy | 0.9973 |
Facial Recognition and Modelling > Face Recognition | LFW | FaceTransformer+OctupletLoss | https://arxiv.org/abs/2207.06726v2 | Accuracy | 0.9973 |
Facial Recognition and Modelling > Face Recognition | LFW | EdgeFace - XS (g=0.6) | https://arxiv.org/abs/2307.01838v2 | Accuracy | 0.9973 |
Facial Recognition and Modelling > Face Recognition | LFW | QMagFace | https://arxiv.org/abs/2111.13475v3 | Accuracy | 0.9850 |
Facial Recognition and Modelling > Face Recognition | LFW | OcularAI-Face | https://arxiv.org/abs/2303.13863v1 | Accuracy | 0.945 |
Facial Recognition and Modelling > Face Recognition | LFW | OcularAI-Face | https://arxiv.org/abs/2303.13863v1 | F1-score | 0.9421 |
Facial Recognition and Modelling > Face Recognition | LFW | OcularAI-Face | https://arxiv.org/abs/2303.13863v1 | Recall | 0.896 |
Facial Recognition and Modelling > Face Recognition | LFW | OcularAI-Face | https://arxiv.org/abs/2303.13863v1 | Precision | 0.9934 |
Facial Recognition and Modelling > Face Recognition | LFW | PIC - MagFace | https://arxiv.org/abs/2211.12483v3 | FNMR [%] @ 10-3 FMR | 0.05 |
Facial Recognition and Modelling > Face Recognition | LFW | PIC - QMagFace | https://arxiv.org/abs/2211.12483v3 | FNMR [%] @ 10-3 FMR | 0.05 |
Facial Recognition and Modelling > Face Recognition | LFW | PIC - ArcFace | https://arxiv.org/abs/2211.12483v3 | FNMR [%] @ 10-3 FMR | 4.38 |
Facial Recognition and Modelling > Face Recognition | CelebA+masks | Fine-tuned ArcFace | https://arxiv.org/abs/2109.01745v5 | Accuracy | 95.43 |
Facial Recognition and Modelling > Face Recognition | CelebA+masks | Fine-tuned FaceNet | https://arxiv.org/abs/2109.01745v5 | Accuracy | 93.58 |
Facial Recognition and Modelling > Face Recognition | CelebA+masks | ArcFace | https://arxiv.org/abs/1801.07698v4 | Accuracy | 91.78 |
Facial Recognition and Modelling > Face Recognition | CelebA+masks | Fine-tuned VGG-Face | https://arxiv.org/abs/2109.01745v5 | Accuracy | 91.51 |
Facial Recognition and Modelling > Face Recognition | CelebA+masks | FaceNet | http://arxiv.org/abs/1503.03832v3 | Accuracy | 90.96 |
Facial Recognition and Modelling > Face Recognition | CelebA+masks | VGG-Face | https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/ | Accuracy | 84.56 |
Facial Recognition and Modelling > Face Recognition | Color FERET | PIC - QMagFace | https://arxiv.org/abs/2211.12483v3 | FNMR [%] @ 10-3 FMR | 3.24 |
Facial Recognition and Modelling > Face Recognition | Color FERET | PIC - MagFace | https://arxiv.org/abs/2211.12483v3 | FNMR [%] @ 10-3 FMR | 3.92 |
Facial Recognition and Modelling > Face Recognition | Color FERET | PIC - ArcFace | https://arxiv.org/abs/2211.12483v3 | FNMR [%] @ 10-3 FMR | 4.22 |
Facial Recognition and Modelling > Face Recognition | Color FERET | VGG based | https://arxiv.org/abs/2401.01227v2 | 5-class test accuracy | 99.2% |
Facial Recognition and Modelling > Face Recognition | Carl | Model with Up Convolution + DoG Filter (Aligned) | https://arxiv.org/abs/2002.04219v1 | Rank-1 | 85 |
Facial Recognition and Modelling > Face Recognition | Carl | DPM | http://arxiv.org/abs/1601.05347v2 | Rank-1 | 71 |
Facial Recognition and Modelling > Face Recognition | UHDB31 | Wang et al. [5] | https://arxiv.org/abs/1911.07538v2 | Rank-1 | 94.5 |
Facial Recognition and Modelling > Face Recognition | UHDB31 | Multi-task | https://arxiv.org/abs/2011.12427v2 | Rank-1 | 84.32 |
Facial Recognition and Modelling > Face Recognition | CFP-FP | GhostFaceNetV2-1 | https://ieeexplore.ieee.org/document/10098610 | Accuracy | 0.9933 |
Facial Recognition and Modelling > Face Recognition | CFP-FP | SymFace + AdaFace + ResNet100 +WebFace | https://arxiv.org/abs/2409.11816v1 | Accuracy | 0.992 |
Facial Recognition and Modelling > Face Recognition | CFP-FP | ElasticFace-Arc | https://arxiv.org/abs/2109.09416v4 | Accuracy | 0.9867 |
Facial Recognition and Modelling > Face Recognition | CFP-FP | DiscFace | https://openaccess.thecvf.com/content/ACCV2020/html/Kim_DiscFace_Minimum_Discrepancy_Learning_for_Deep_Face_Recognition_ACCV_2020_paper.html | Accuracy | 0.9854 |
Facial Recognition and Modelling > Face Recognition | CFP-FP | CircleLoss(ours) | https://arxiv.org/abs/2002.10857v2 | Accuracy | 0.9602 |
Facial Recognition and Modelling > Face Recognition | CFP-FP | EdgeFace - S (g=0.5) | https://arxiv.org/abs/2307.01838v2 | Accuracy | 0.9581 |
Facial Recognition and Modelling > Face Recognition | CFP-FP | DCQ | https://arxiv.org/abs/2105.11113v1 | Accuracy | 0.9287 |
Facial Recognition and Modelling > Face Recognition | CFP-FP | QMagFace | https://arxiv.org/abs/2111.13475v3 | Accuracy | 0.8395 |
Facial Recognition and Modelling > Face Recognition | MFW+ (M-M) | ArcFace + PPL | https://bmvc2022.mpi-inf.mpg.de/723/ | TAR@FAR=0.0001 | 78.80 |
Facial Recognition and Modelling > Face Recognition | MFW+ (M-M) | MaskInv-HG | https://bmvc2022.mpi-inf.mpg.de/723/ | TAR@FAR=0.0001 | 78.36 |
Facial Recognition and Modelling > Face Recognition | MFW+ (M-M) | FocusFace | https://bmvc2022.mpi-inf.mpg.de/723/ | TAR@FAR=0.0001 | 77.77 |
Facial Recognition and Modelling > Face Recognition | Color FERET (Online Open Set) | FaceNet+Adaptive Threshold | http://arxiv.org/abs/1810.11160v1 | Average Accuracy (10 times) | 83.79 |
Facial Recognition and Modelling > Face Recognition | Color FERET (Online Open Set) | FaceNet+Fixed Threshold (0.3968) | http://arxiv.org/abs/1810.11160v1 | Average Accuracy (10 times) | 80.72 |
Facial Recognition and Modelling > Face Recognition | EURECOM | Model with Up Convolution + DoG Filter | https://arxiv.org/abs/2002.04219v1 | Rank-1 | 88.33 |
Facial Recognition and Modelling > Face Recognition | CASIA-WebFace+masks | Fine-tuned ArcFace | https://arxiv.org/abs/2109.01745v5 | Accuracy | 91.47 |
Facial Recognition and Modelling > Face Recognition | CASIA-WebFace+masks | Fine-tuned FaceNet | https://arxiv.org/abs/2109.01745v5 | Accuracy | 88.06 |
Facial Recognition and Modelling > Face Recognition | CASIA-WebFace+masks | ArcFace | https://arxiv.org/abs/1801.07698v4 | Accuracy | 87.95 |
Facial Recognition and Modelling > Face Recognition | CASIA-WebFace+masks | Fine-tuned VGG-Face | https://arxiv.org/abs/2109.01745v5 | Accuracy | 86.85 |
Facial Recognition and Modelling > Face Recognition | CASIA-WebFace+masks | FaceNet | http://arxiv.org/abs/1503.03832v3 | Accuracy | 84.21 |
Facial Recognition and Modelling > Face Recognition | CASIA-WebFace+masks | VGG-Face | https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/ | Accuracy | 79.65 |
Facial Recognition and Modelling > Face Recognition | LFW (Online Open Set) | FaceNet+Adaptive Threshold | http://arxiv.org/abs/1810.11160v1 | Average Accuracy (10 times) | 76.46 |
Facial Recognition and Modelling > Face Recognition | LFW (Online Open Set) | FaceNet+Fixed Threshold (0.3779) | http://arxiv.org/abs/1810.11160v1 | Average Accuracy (10 times) | 53.97 |
Facial Recognition and Modelling > Face Recognition | Adience (Online Open Set) | FaceNet+Adaptive Threshold | http://arxiv.org/abs/1810.11160v1 | Average Accuracy (10 times) | 84.3 |
Facial Recognition and Modelling > Face Recognition | Adience (Online Open Set) | FaceNet+Fixed Threshold (0.2487) | http://arxiv.org/abs/1810.11160v1 | Average Accuracy (10 times) | 80.6 |
Facial Recognition and Modelling > Face Recognition | MLFW | MS1MV2, R100, SFace | https://arxiv.org/abs/2109.05804v2 | Accuracy | 91.57 |
Facial Recognition and Modelling > Face Recognition | MLFW | MS1MV2, R100, Arcface | https://arxiv.org/abs/2109.05804v2 | Accuracy | 90.57 |
Facial Recognition and Modelling > Face Recognition | MLFW | MS1MV2, R100, Curricularface | https://arxiv.org/abs/2109.05804v2 | Accuracy | 90.43 |
Facial Recognition and Modelling > Face Recognition | MLFW | VGGFace2, R50, ArcFace | https://arxiv.org/abs/2109.05804v2 | Accuracy | 85.95 |
Facial Recognition and Modelling > Face Recognition | MLFW | CASIA-WebFace, R50, CosFace | https://arxiv.org/abs/2109.05804v2 | Accuracy | 82.52 |
Facial Recognition and Modelling > Face Recognition | MLFW | Private-Asia, R50, ArcFace | https://arxiv.org/abs/2109.05804v2 | Accuracy | 77.20 |
Facial Recognition and Modelling > Face Recognition | CPLFW | GhostFaceNetV2-1 | https://ieeexplore.ieee.org/document/10098610 | Accuracy | 0.9465 |
Facial Recognition and Modelling > Face Recognition | CPLFW | ElasticFace-Arc | https://arxiv.org/abs/2109.09416v4 | Accuracy | 0.9327 |
Facial Recognition and Modelling > Face Recognition | CALFW | Prodpoly | https://arxiv.org/abs/2006.13026v2 | Accuracy | 0.96233 |
Facial Recognition and Modelling > Face Recognition | CALFW | ElasticFace-Arc | https://arxiv.org/abs/2109.09416v4 | Accuracy | 0.9617 |
Facial Recognition and Modelling > Face Recognition | CALFW | GhostFaceNetV2-1 | https://ieeexplore.ieee.org/document/10098610 | Accuracy | 0.9612 |
Facial Recognition and Modelling > Face Recognition | CFP-FF | GhostFaceNetV2-1 | https://ieeexplore.ieee.org/document/10098610 | Accuracy | 99.9143 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | PIQ23 | Dual-Branch Network | https://arxiv.org/abs/2405.08555v1 | SRCC | 0.85 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | PIQ23 | Dual-Branch Network | https://arxiv.org/abs/2405.08555v1 | PLCC | 0.86 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | PIQ23 | Dual-Branch Network | https://arxiv.org/abs/2405.08555v1 | KRCC | 0.68 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | PIQ23 | Dual-Branch Network | https://arxiv.org/abs/2405.08555v1 | MAE | 0.53 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | PIQ23 | PICNIQ | https://arxiv.org/abs/2403.09746v2 | SRCC | 0.81 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | PIQ23 | PICNIQ | https://arxiv.org/abs/2403.09746v2 | PLCC | 0.82 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | PIQ23 | PICNIQ | https://arxiv.org/abs/2403.09746v2 | KRCC | 0.62 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | PIQ23 | PICNIQ | https://arxiv.org/abs/2403.09746v2 | MAE | 0.72 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | PIQ23 | FHIQA | https://arxiv.org/abs/2402.09178v1 | SRCC | 0.78 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | PIQ23 | FHIQA | https://arxiv.org/abs/2402.09178v1 | PLCC | 0.78 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | PIQ23 | FHIQA | https://arxiv.org/abs/2402.09178v1 | KRCC | 0.59 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | PIQ23 | FHIQA | https://arxiv.org/abs/2402.09178v1 | MAE | 1.12 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | CGFIQA-40k | DSL-FIQA | https://arxiv.org/abs/2406.09622v1 | PLCC | 0.9745 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | CGFIQA-40k | DSL-FIQA | https://arxiv.org/abs/2406.09622v1 | SRCC | 0.9740 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | CGFIQA-40k | StyleGAN-IQA | https://arxiv.org/abs/2207.04904v2 | PLCC | 0.9673 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | CGFIQA-40k | StyleGAN-IQA | https://arxiv.org/abs/2207.04904v2 | SRCC | 0.9684 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | CGFIQA-40k | IFQA | https://arxiv.org/abs/2211.07077v2 | PLCC | 0.9601 |
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment | CGFIQA-40k | IFQA | https://arxiv.org/abs/2211.07077v2 | SRCC | 0.9603 |
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