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⌀ |
|---|---|---|---|---|---|
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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