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 Reconstruction > 3D Face Reconstruction | REALY (side-view) | MICA | https://arxiv.org/abs/2204.06607v2 | @cheek | 1.109 (±0.325) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | RingNet | https://arxiv.org/abs/1905.06817v1 | @nose | 1.921 (±0.451) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | RingNet | https://arxiv.org/abs/1905.06817v1 | all | 2.256 |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | RingNet | https://arxiv.org/abs/1905.06817v1 | @mouth | 1.994 (±0.604) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | RingNet | https://arxiv.org/abs/1905.06817v1 | @forehead | 3.081 (±0.950) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | RingNet | https://arxiv.org/abs/1905.06817v1 | @cheek | 2.027 (±0.710) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | DECA-f | https://arxiv.org/abs/2012.04012v2 | @nose | 2.286 (±1.103) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | DECA-f | https://arxiv.org/abs/2012.04012v2 | all | 2.261 |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | DECA-f | https://arxiv.org/abs/2012.04012v2 | @mouth | 2.684 (±1.041) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | DECA-f | https://arxiv.org/abs/2012.04012v2 | @forehead | 2.519 (±0.718) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | DECA-f | https://arxiv.org/abs/2012.04012v2 | @cheek | 1.555 (±0.822) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | EMOCA-f | https://arxiv.org/abs/2204.11312v1 | @nose | 2.455 (±0.636) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | EMOCA-f | https://arxiv.org/abs/2204.11312v1 | all | 2.402 |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | EMOCA-f | https://arxiv.org/abs/2204.11312v1 | @mouth | 2.948 (±1.292) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | EMOCA-f | https://arxiv.org/abs/2204.11312v1 | @forehead | 2.606 (±0.686) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | EMOCA-f | https://arxiv.org/abs/2204.11312v1 | @cheek | 1.599 (±0.563) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | ExpNet | http://arxiv.org/abs/1802.00542v1 | @nose | 2.508 (±0.491) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | ExpNet | http://arxiv.org/abs/1802.00542v1 | all | 2.476 |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | ExpNet | http://arxiv.org/abs/1802.00542v1 | @mouth | 2.160 (±0.448) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | ExpNet | http://arxiv.org/abs/1802.00542v1 | @forehead | 3.393 (±1.076) |
Facial Recognition and Modelling > Face Reconstruction > 3D Face Reconstruction | REALY (side-view) | ExpNet | http://arxiv.org/abs/1802.00542v1 | @cheek | 1.842 (±0.609) |
Facial Recognition and Modelling > Face Anti-Spoofing | MLFP | MCCNN (BCE+OCCL)-GMM | https://arxiv.org/abs/2007.11457v1 | HTER | 3.4 |
Facial Recognition and Modelling > Face Anti-Spoofing | CASIA-MFSD | 3D Synthesis (balancing sampling) | https://arxiv.org/abs/1901.00488v3 | EER | 2.22 |
Facial Recognition and Modelling > Face Anti-Spoofing | CASIA-MFSD | 3D Synthesis (balancing sampling) | https://arxiv.org/abs/1901.00488v3 | HTER | 1.67 |
Facial Recognition and Modelling > Face Anti-Spoofing | CASIA-MFSD | Multi-Scale | https://arxiv.org/abs/1408.5601v2 | EER | 4.92 |
Facial Recognition and Modelling > Face Anti-Spoofing | SiW-Enroll5 | ResNet18 Personalized | https://openaccess.thecvf.com/content/WACV2022W/MAP-A/html/Belli_A_Personalized_Benchmark_for_Face_Anti-Spoofing_WACVW_2022_paper.html | AUC | 99.2 |
Facial Recognition and Modelling > Face Anti-Spoofing | SiW-Enroll5 | FeatherNet Personalized | https://openaccess.thecvf.com/content/WACV2022W/MAP-A/html/Belli_A_Personalized_Benchmark_for_Face_Anti-Spoofing_WACVW_2022_paper.html | AUC | 99.0 |
Facial Recognition and Modelling > Face Anti-Spoofing | SiW-Enroll5 | FeatherNet | http://arxiv.org/abs/1904.09290v1 | AUC | 98.9 |
Facial Recognition and Modelling > Face Anti-Spoofing | SiW-Enroll5 | VGG16 Personalized | https://openaccess.thecvf.com/content/WACV2022W/MAP-A/html/Belli_A_Personalized_Benchmark_for_Face_Anti-Spoofing_WACVW_2022_paper.html | AUC | 98.1 |
Facial Recognition and Modelling > Face Anti-Spoofing | SiW-Enroll5 | VGG16 | http://arxiv.org/abs/1409.1556v6 | AUC | 97.8 |
Facial Recognition and Modelling > Face Anti-Spoofing | OULU-NPU | Bi-FPNFAS | https://www.mdpi.com/1424-8220/21/8/2799/htm | ACER | 2.92 |
Facial Recognition and Modelling > Face Anti-Spoofing | OULU-NPU | Entry-V2 | https://arxiv.org/abs/2206.06510v1 | ACER | 3.2 |
Facial Recognition and Modelling > Face Anti-Spoofing | OULU-NPU | Entry-V2 | https://arxiv.org/abs/2206.06510v1 | HTER | 2.6 |
Facial Recognition and Modelling > Face Anti-Spoofing | OULU-NPU | A-DeepPixBis | https://ieeexplore.ieee.org/abstract/document/9363382 | ACER | 5.22 |
Facial Recognition and Modelling > Face Anti-Spoofing | OULU-NPU | CDCN | https://arxiv.org/abs/2003.04092v1 | ACER | 6.9 |
Facial Recognition and Modelling > Face Anti-Spoofing | CelebA-Spoof-Enroll5 | ResNet 18 Personalized | https://openaccess.thecvf.com/content/WACV2022W/MAP-A/html/Belli_A_Personalized_Benchmark_for_Face_Anti-Spoofing_WACVW_2022_paper.html | AUC | 99.2 |
Facial Recognition and Modelling > Face Anti-Spoofing | CelebA-Spoof-Enroll5 | VGG16 Personalized | https://openaccess.thecvf.com/content/WACV2022W/MAP-A/html/Belli_A_Personalized_Benchmark_for_Face_Anti-Spoofing_WACVW_2022_paper.html | AUC | 98.6 |
Facial Recognition and Modelling > Face Anti-Spoofing | CelebA-Spoof-Enroll5 | VGG16 | http://arxiv.org/abs/1409.1556v6 | AUC | 98.0 |
Facial Recognition and Modelling > Face Anti-Spoofing | CelebA-Spoof-Enroll5 | FeatherNet Personalized | https://openaccess.thecvf.com/content/WACV2022W/MAP-A/html/Belli_A_Personalized_Benchmark_for_Face_Anti-Spoofing_WACVW_2022_paper.html | AUC | 97.8 |
Facial Recognition and Modelling > Face Anti-Spoofing | CelebA-Spoof-Enroll5 | FeatherNet | http://arxiv.org/abs/1904.09290v1 | AUC | 97.1 |
Facial Recognition and Modelling > Face Anti-Spoofing | SiW (Protocol 3) | FasTCo + OAP | https://arxiv.org/abs/2207.12272v1 | ACER | 21.7 |
Facial Recognition and Modelling > Face Anti-Spoofing | SiW (Protocol 3) | ResNet50 + OAP | https://arxiv.org/abs/2207.12272v1 | ACER | 22.9 |
Facial Recognition and Modelling > Face Anti-Spoofing | SiW (Protocol 3) | FeatherNet + OAP | https://arxiv.org/abs/2207.12272v1 | ACER | 24.3 |
Facial Recognition and Modelling > Face Anti-Spoofing | SiW (Protocol 3) | FasTCo | https://arxiv.org/abs/2006.06756v1 | ACER | 28.7 |
Facial Recognition and Modelling > Face Anti-Spoofing | SiW (Protocol 3) | CDCN++ + OAP | https://arxiv.org/abs/2207.12272v1 | ACER | 28.7 |
Facial Recognition and Modelling > Face Anti-Spoofing | SiW (Protocol 3) | FeatherNet | http://arxiv.org/abs/1904.09290v1 | ACER | 31.1 |
Facial Recognition and Modelling > Face Anti-Spoofing | SiW (Protocol 3) | CDCN++ | https://arxiv.org/abs/2003.04092v1 | ACER | 40.2 |
Facial Recognition and Modelling > Face Anti-Spoofing | Replay-Attack | Entry-V2 | https://arxiv.org/abs/2206.06510v1 | EER | 0 |
Facial Recognition and Modelling > Face Anti-Spoofing | Replay-Attack | Entry-V2 | https://arxiv.org/abs/2206.06510v1 | HTER | 0 |
Facial Recognition and Modelling > Face Anti-Spoofing | Replay-Attack | 3D Synthesis (balancing sampling) | https://arxiv.org/abs/1901.00488v3 | EER | 0.25 |
Facial Recognition and Modelling > Face Anti-Spoofing | Replay-Attack | 3D Synthesis (balancing sampling) | https://arxiv.org/abs/1901.00488v3 | HTER | 0.63 |
Facial Recognition and Modelling > Face Anti-Spoofing | Replay-Attack | YCbCr+HSV-LBP | http://arxiv.org/abs/1511.06316v1 | EER | 0.40 |
Facial Recognition and Modelling > Face Anti-Spoofing | Replay-Attack | YCbCr+HSV-LBP | http://arxiv.org/abs/1511.06316v1 | HTER | 2.90 |
Facial Recognition and Modelling > Face Anti-Spoofing | Replay-Attack | Multi-Scale | https://arxiv.org/abs/1408.5601v2 | EER | 2.14 |
Facial Recognition and Modelling > Face Anti-Spoofing | MSU-MFSD | Entry-V2 | https://arxiv.org/abs/2206.06510v1 | Equal Error Rate | 0 |
Facial Recognition and Modelling > Face Anti-Spoofing | MSU-MFSD | Entry-V2 | https://arxiv.org/abs/2206.06510v1 | HTER | 0 |
Facial Recognition and Modelling > Face Anti-Spoofing | MSU-MFSD | GFA-CNN | http://arxiv.org/abs/1901.05602v1 | Equal Error Rate | 7.5% |
Facial Recognition and Modelling > Face Anti-Spoofing | MSU-MFSD | Color LBP | http://arxiv.org/abs/1511.06316v1 | Equal Error Rate | 10.8% |
Facial Recognition and Modelling > Facial Landmark Detection | AFLW2000-3D | JVCR | http://arxiv.org/abs/1801.09242v1 | GTE | 7.28 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W (Full) | TS3 | https://arxiv.org/abs/1908.02116v3 | Mean NME | 3.49 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W (Full) | AnchorFace | https://arxiv.org/abs/2007.03221v3 | Mean NME | 3.72 |
Facial Recognition and Modelling > Facial Landmark Detection | CatFLW | ELD (EfficientNetV2S) | https://arxiv.org/abs/2310.09793v2 | NME | 2.83 |
Facial Recognition and Modelling > Facial Landmark Detection | CatFLW | ELD (EfficientNetV2B0) | https://arxiv.org/abs/2310.09793v2 | NME | 2.98 |
Facial Recognition and Modelling > Facial Landmark Detection | CatFLW | ELD (MobileNetV2) | https://arxiv.org/abs/2310.09793v2 | NME | 3.09 |
Facial Recognition and Modelling > Facial Landmark Detection | AFLW-Front | FiFA | https://arxiv.org/abs/2402.15044v1 | Mean NME | 0.80 |
Facial Recognition and Modelling > Facial Landmark Detection | AFLW-Front | FiFA | https://arxiv.org/abs/2402.15044v1 | Mean NME | 0.80 |
Facial Recognition and Modelling > Facial Landmark Detection | AFLW-Front | FiFA | https://arxiv.org/abs/2402.15044v1 | NME | 0.80 |
Facial Recognition and Modelling > Facial Landmark Detection | AFLW-Front | AnchorFace | https://arxiv.org/abs/2007.03221v3 | Mean NME | 1.38 |
Facial Recognition and Modelling > Facial Landmark Detection | AFLW-Front | SAN | http://arxiv.org/abs/1803.04108v4 | Mean NME | 1.85 |
Facial Recognition and Modelling > Facial Landmark Detection | COCO-WholeBody | HPRNet (Hourglass-104) | https://arxiv.org/abs/2106.04269v2 | keypoint AP | 75.4 |
Facial Recognition and Modelling > Facial Landmark Detection | COCO-WholeBody | HPRNet (DLA) | https://arxiv.org/abs/2106.04269v2 | keypoint AP | 74.6 |
Facial Recognition and Modelling > Facial Landmark Detection | WFLW | D-ViT | https://arxiv.org/abs/2411.07167v1 | NME (inter-ocular) | 3.75 |
Facial Recognition and Modelling > Facial Landmark Detection | WFLW | D-ViT | https://arxiv.org/abs/2411.07167v1 | AUC@10 (inter-ocular) | 63.7 |
Facial Recognition and Modelling > Facial Landmark Detection | WFLW | D-ViT | https://arxiv.org/abs/2411.07167v1 | FR@10 (inter-ocular) | 1.76 |
Facial Recognition and Modelling > Facial Landmark Detection | WFLW | D-ViT | https://arxiv.org/abs/2411.07167v1 | NME | 3.75 |
Facial Recognition and Modelling > Facial Landmark Detection | WFLW | ELD (EfficientNetV2B1) | https://arxiv.org/abs/2310.09793v2 | NME | 4.65 |
Facial Recognition and Modelling > Facial Landmark Detection | WFLW | FiFA | https://arxiv.org/abs/2402.15044v1 | NME (inter-ocular) | 3.89 |
Facial Recognition and Modelling > Facial Landmark Detection | WFLW | FiFA | https://arxiv.org/abs/2402.15044v1 | AUC@10 (inter-ocular) | 61.78 |
Facial Recognition and Modelling > Facial Landmark Detection | WFLW | FiFA | https://arxiv.org/abs/2402.15044v1 | FR@10 (inter-ocular) | 1.60 |
Facial Recognition and Modelling > Facial Landmark Detection | 300-VW (C) | CPM+SBR+PAM | http://arxiv.org/abs/1807.00966v2 | AUC0.08 private | 59.39 |
Facial Recognition and Modelling > Facial Landmark Detection | 300-VW (C) | CPM+SBR | http://arxiv.org/abs/1807.00966v2 | AUC0.08 private | 58.22 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | D-ViT | https://arxiv.org/abs/2411.07167v1 | NME | 2.85 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | FiFA | https://arxiv.org/abs/2402.15044v1 | NME | 2.89 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | SPIGA (Inter-ocular Norm) | https://arxiv.org/abs/2210.07233v1 | NME | 2.99 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | AnchorFace | https://arxiv.org/abs/2007.03221v3 | NME | 3.12 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | 3DDE (Inter-ocular Norm) | https://arxiv.org/abs/1902.01831v2 | NME | 3.13 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | DCFE (Inter-ocular Norm) | http://openaccess.thecvf.com/content_ECCV_2018/html/Roberto_Valle_A_Deeply-initialized_Coarse-to-fine_ECCV_2018_paper.html | NME | 3.24 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | CHR2C (Inter-ocular Norm) | https://doi.org/10.1016/j.patrec.2019.10.012 | NME | 3.3 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | CNN-CRF (Inter-ocular Norm) | https://arxiv.org/abs/2010.09035v1 | NME | 3.30 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | Adaloss | https://arxiv.org/abs/1908.01070v1 | NME | 3.31 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | TS3 | https://arxiv.org/abs/1908.02116v3 | NME | 3.49 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | SAN GT | http://arxiv.org/abs/1803.04108v4 | NME | 3.98 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | CFSS | http://arxiv.org/abs/1511.07212v1 | NME | 5.76 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | Pose-Invariant | http://arxiv.org/abs/1707.06286v1 | NME | 6.30 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | 3DDFA | http://arxiv.org/abs/1511.07212v1 | NME | 7.01 |
Facial Recognition and Modelling > Facial Landmark Detection | 300W | FPN | http://arxiv.org/abs/1708.07517v2 | Mean Error Rate | 0.1043 |
Facial Recognition and Modelling > Facial Landmark Detection | COFW | D-ViT | https://arxiv.org/abs/2411.07167v1 | NME (inter-pupil) | 4.13 |
Facial Recognition and Modelling > Facial Landmark Detection | COFW | FiFA | https://arxiv.org/abs/2402.15044v1 | NME | 2.96 |
Facial Recognition and Modelling > Facial Landmark Detection | COFW | FiFA | https://arxiv.org/abs/2402.15044v1 | NME (inter-ocular) | 2.96 |
Facial Recognition and Modelling > Facial Landmark Detection | AFLW-Full | FiFA | https://arxiv.org/abs/2402.15044v1 | Mean NME | 0.92 |
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