task_path
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⌀ |
|---|---|---|---|---|---|
Facial Recognition and Modelling > Face Verification
|
MegaFace
|
SV-AM-Softmax
|
http://arxiv.org/abs/1812.11317v1
|
Accuracy
|
97.38%
|
Facial Recognition and Modelling > Face Verification
|
MegaFace
|
CosFace
|
http://arxiv.org/abs/1801.09414v2
|
Accuracy
|
96.65%
|
Facial Recognition and Modelling > Face Verification
|
MegaFace
|
PFEfuse + match
|
https://arxiv.org/abs/1904.09658v4
|
Accuracy
|
92.51%
|
Facial Recognition and Modelling > Face Verification
|
MegaFace
|
SphereFace (3-patch ensemble)
|
http://arxiv.org/abs/1704.08063v4
|
Accuracy
|
89.142%
|
Facial Recognition and Modelling > Face Verification
|
MegaFace
|
SphereFace (single model)
|
http://arxiv.org/abs/1704.08063v4
|
Accuracy
|
85.561%
|
Facial Recognition and Modelling > Face Verification
|
MegaFace
|
Light CNN-29
|
http://arxiv.org/abs/1511.02683v4
|
Accuracy
|
85.133%
|
Facial Recognition and Modelling > Face Verification
|
CK+
|
SphereFace
|
http://arxiv.org/abs/1704.08063v4
|
Accuracy
|
93.80
|
Facial Recognition and Modelling > Face Verification
|
Trillion Pairs Dataset
|
SV-AM-Softmax
|
http://arxiv.org/abs/1812.11317v1
|
Accuracy
|
72.71
|
Facial Recognition and Modelling > Face Verification
|
Trillion Pairs Dataset
|
AM-Softmax
|
http://arxiv.org/abs/1801.05599v4
|
Accuracy
|
61.61
|
Facial Recognition and Modelling > Face Verification
|
Trillion Pairs Dataset
|
Arc-Softmax
|
https://arxiv.org/abs/1801.07698v4
|
Accuracy
|
57.45
|
Facial Recognition and Modelling > Face Verification
|
Trillion Pairs Dataset
|
A-Softmax
|
http://arxiv.org/abs/1704.08063v4
|
Accuracy
|
43.76
|
Facial Recognition and Modelling > Face Verification
|
Trillion Pairs Dataset
|
F-Softmax
|
http://arxiv.org/abs/1708.02002v2
|
Accuracy
|
37.14
|
Facial Recognition and Modelling > Face Verification
|
Trillion Pairs Dataset
|
HM-Softmax
|
http://arxiv.org/abs/1604.03540v1
|
Accuracy
|
34.46
|
Facial Recognition and Modelling > Face Verification
|
BUAA-VisNir
|
LightCNN-29 + DVG
|
https://arxiv.org/abs/1903.10203v3
|
TAR @ FAR=0.001
|
97.3
|
Facial Recognition and Modelling > Face Verification
|
BUAA-VisNir
|
LightCNN-29 + DVG
|
https://arxiv.org/abs/1903.10203v3
|
TAR @ FAR=0.01
|
98.5
|
Facial Recognition and Modelling > Face Verification
|
BUAA-VisNir
|
DVR Wu et al. (2019)
|
http://arxiv.org/abs/1809.01936v3
|
TAR @ FAR=0.001
|
96.9
|
Facial Recognition and Modelling > Face Verification
|
BUAA-VisNir
|
DVR Wu et al. (2019)
|
http://arxiv.org/abs/1809.01936v3
|
TAR @ FAR=0.01
|
98.5
|
Facial Recognition and Modelling > Face Verification
|
BUAA-VisNir
|
W-CNN He et al. (2018)
|
http://arxiv.org/abs/1708.02412v1
|
TAR @ FAR=0.001
|
91.9
|
Facial Recognition and Modelling > Face Verification
|
BUAA-VisNir
|
W-CNN He et al. (2018)
|
http://arxiv.org/abs/1708.02412v1
|
TAR @ FAR=0.01
|
96.0
|
Facial Recognition and Modelling > Face Verification
|
Labeled Faces in the Wild
|
ArcFace + MS1MV2 + R100,
|
https://arxiv.org/abs/1801.07698v4
|
Accuracy
|
99.83%
|
Facial Recognition and Modelling > Face Verification
|
Labeled Faces in the Wild
|
FaceNet
|
http://arxiv.org/abs/1503.03832v3
|
Accuracy
|
99.63%
|
Facial Recognition and Modelling > Face Verification
|
Labeled Faces in the Wild
|
Dlib
|
https://www.jmlr.org/papers/volume10/king09a/king09a.pdf
|
Accuracy
|
99.38%
|
Facial Recognition and Modelling > Face Verification
|
Labeled Faces in the Wild
|
VGG-Face
|
https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/
|
Accuracy
|
98.78%
|
Facial Recognition and Modelling > Face Verification
|
Labeled Faces in the Wild
|
DeepFace
|
https://research.fb.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/
|
Accuracy
|
98.37%
|
Facial Recognition and Modelling > Face Verification
|
Labeled Faces in the Wild
|
DeepID
|
http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf
|
Accuracy
|
97.05%
|
Facial Recognition and Modelling > Face Verification
|
Labeled Faces in the Wild
|
OpenFace
|
http://reports-archive.adm.cs.cmu.edu/anon/anon/2016/CMU-CS-16-118.pdf
|
Accuracy
|
92.92%
|
Facial Recognition and Modelling > Face Verification
|
Oulu-CASIA
|
DeepId2+
|
https://arxiv.org/abs/1412.1265v1
|
Accuracy
|
96.50
|
Facial Recognition and Modelling > Face Verification
|
IIIT-D Viewed Sketch
|
LightCNN-29 + DVG
|
https://arxiv.org/abs/1903.10203v3
|
TAR @ FAR=0.01
|
97.86
|
Facial Recognition and Modelling > Face Verification
|
IJB-S
|
AdaFace+CSFM
|
https://arxiv.org/abs/2207.10180v1
|
Rank-1 (Video2Single)
|
72.54
|
Facial Recognition and Modelling > Face Verification
|
IJB-S
|
AdaFace+CSFM
|
https://arxiv.org/abs/2207.10180v1
|
Rank-1 (Video2Booking)
|
72.65
|
Facial Recognition and Modelling > Face Verification
|
IJB-S
|
AdaFace+CSFM
|
https://arxiv.org/abs/2207.10180v1
|
Rank-1 (Video2Video)
|
39.14
|
Facial Recognition and Modelling > Face Verification
|
IJB-S
|
ArcFace+CSFM
|
https://arxiv.org/abs/2207.10180v1
|
Rank-1 (Video2Single)
|
63.86
|
Facial Recognition and Modelling > Face Verification
|
IJB-S
|
ArcFace+CSFM
|
https://arxiv.org/abs/2207.10180v1
|
Rank-1 (Video2Booking)
|
65.95
|
Facial Recognition and Modelling > Face Verification
|
IJB-S
|
ArcFace+CSFM
|
https://arxiv.org/abs/2207.10180v1
|
Rank-1 (Video2Video)
|
21.38
|
Facial Recognition and Modelling > Face Verification
|
QMUL-SurvFace
|
DiscFace
|
https://openaccess.thecvf.com/content/ACCV2020/html/Kim_DiscFace_Minimum_Discrepancy_Learning_for_Deep_Face_Recognition_ACCV_2020_paper.html
|
TAR @ FAR=0.1
|
35.9
|
Facial Recognition and Modelling > Face Verification
|
CASIA NIR-VIS 2.0
|
LightCNN-29 + DVG
|
https://arxiv.org/abs/1903.10203v3
|
TAR @ FAR=0.001
|
99.8
|
Facial Recognition and Modelling > Face Verification
|
CASIA NIR-VIS 2.0
|
DVR Wu et al. (2019)
|
http://arxiv.org/abs/1809.01936v3
|
TAR @ FAR=0.001
|
99.6
|
Facial Recognition and Modelling > Face Verification
|
CASIA NIR-VIS 2.0
|
W-CNN He et al. (2018)
|
http://arxiv.org/abs/1708.02412v1
|
TAR @ FAR=0.001
|
98.4
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
QMagFace
|
https://arxiv.org/abs/2111.13475v3
|
TAR @ FAR=0.01
|
97.72%
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
QMagFace
|
https://arxiv.org/abs/2111.13475v3
|
TAR @ FAR=0.001
|
96.48
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
QMagFace
|
https://arxiv.org/abs/2111.13475v3
|
TAR@FAR=0.0001
|
94.7
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
QMagFace
|
https://arxiv.org/abs/2111.13475v3
|
TAR @ FAR=0.0001
|
94.7
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
Arc+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR @ FAR=0.01
|
97.7%
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
Arc+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR @ FAR=0.001
|
96.6
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
Arc+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR@FAR=0.0001
|
95.04
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
Mag+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR @ FAR=0.01
|
97.63%
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
Mag+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR @ FAR=0.001
|
96.5
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
Mag+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR@FAR=0.0001
|
95.21
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
Cos+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR @ FAR=0.01
|
97.36%
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
Cos+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR @ FAR=0.001
|
96.5
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
Cos+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR@FAR=0.0001
|
94.99
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
FPN
|
http://arxiv.org/abs/1708.07517v2
|
TAR @ FAR=0.01
|
96.5%
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
SE-GV-3-g2
|
http://arxiv.org/abs/1810.09951v1
|
TAR @ FAR=0.01
|
96.4%
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
VGGFace2_ft
|
http://arxiv.org/abs/1710.08092v2
|
TAR @ FAR=0.01
|
95.6%
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
VGGFace2_ft
|
http://arxiv.org/abs/1710.08092v2
|
TAR @ FAR=0.001
|
90.8
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
CAFace+AdaFace (WebFace4M)
|
https://arxiv.org/abs/2210.10864v3
|
TAR @ FAR=0.001
|
96.91
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
CAFace+AdaFace (WebFace4M)
|
https://arxiv.org/abs/2210.10864v3
|
TAR@FAR=0.0001
|
95.53
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
CAFace+AdaFace (WebFace4M)
|
https://arxiv.org/abs/2210.10864v3
|
TAR @ FAR=1e-5
|
92.29
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
PartialFC(WebFace42M)
|
https://arxiv.org/abs/2203.15565v1
|
TAR@FAR=0.0001
|
96.71
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
AdaFace (WebFace4M)
|
https://arxiv.org/abs/2204.00964v2
|
TAR@FAR=0.0001
|
96.03
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
AdaFace (MS1MV3)
|
https://arxiv.org/abs/2204.00964v2
|
TAR@FAR=0.0001
|
95.84
|
Facial Recognition and Modelling > Face Verification
|
IJB-B
|
AdaFace (MS1MV2)
|
https://arxiv.org/abs/2204.00964v2
|
TAR@FAR=0.0001
|
95.67
|
Facial Recognition and Modelling > Face Verification
|
CPLFW
|
DiscFace
|
https://openaccess.thecvf.com/content/ACCV2020/html/Kim_DiscFace_Minimum_Discrepancy_Learning_for_Deep_Face_Recognition_ACCV_2020_paper.html
|
Accuracy
|
93.37
|
Facial Recognition and Modelling > Face Verification
|
CPLFW
|
SFace
|
https://arxiv.org/abs/2205.12010v1
|
Accuracy
|
91.05%
|
Facial Recognition and Modelling > Face Verification
|
BTS3.1
|
ProxyFusion (Adaface)
|
https://proceedings.neurips.cc/paper_files/paper/2024/hash/81f554467f27759e88de14ba2fbafb47-Abstract-Conference.html
|
TAR @ FAR=0.01
|
0.689
|
Facial Recognition and Modelling > Face Verification
|
BTS3.1
|
CoNAN (Adaface)
|
https://arxiv.org/abs/2307.10237v1
|
TAR @ FAR=0.01
|
0.5632
|
Facial Recognition and Modelling > Face Verification
|
BTS3.1
|
NAN (Adaface)
|
http://arxiv.org/abs/1603.05474v4
|
TAR @ FAR=0.01
|
0.5444
|
Facial Recognition and Modelling > Face Verification
|
BTS3.1
|
MCN (Adaface)
|
http://arxiv.org/abs/1807.09192v1
|
TAR @ FAR=0.01
|
0.5425
|
Facial Recognition and Modelling > Face Verification
|
BTS3.1
|
CAFace (Adaface)
|
https://arxiv.org/abs/2210.10864v3
|
TAR @ FAR=0.01
|
0.5131
|
Facial Recognition and Modelling > Face Verification
|
BTS3.1
|
MCN (Arcface)
|
http://arxiv.org/abs/1603.05474v4
|
TAR @ FAR=0.01
|
0.3941
|
Facial Recognition and Modelling > Face Verification
|
BTS3.1
|
NAN (Arcface)
|
http://arxiv.org/abs/1603.05474v4
|
TAR @ FAR=0.01
|
0.3901
|
Facial Recognition and Modelling > Face Verification > Disguised Face Verification
|
MegaFace
|
FaceNet
|
http://arxiv.org/abs/1503.03832v3
|
Accuracy
|
86.47
|
Facial Recognition and Modelling > Face Verification > Disguised Face Verification
|
Disguised Faces in the Wild
|
DisguiseNet
|
http://arxiv.org/abs/1804.09669v2
|
GAR @0.1% FAR
|
23.25
|
Facial Recognition and Modelling > Face Verification > Disguised Face Verification
|
Disguised Faces in the Wild
|
DisguiseNet
|
http://arxiv.org/abs/1804.09669v2
|
GAR @1% FAR
|
60.89
|
Facial Recognition and Modelling > Face Verification > Disguised Face Verification
|
Disguised Faces in the Wild
|
DisguiseNet
|
http://arxiv.org/abs/1804.09669v2
|
GAR @10% FAR
|
98.99
|
Facial Recognition and Modelling > Face Verification > Disguised Face Verification
|
Disguised Faces in the Wild
|
VGG-Face model features + cosine similarity metric
|
http://arxiv.org/abs/1811.08837v1
|
GAR @0.1% FAR
|
17.73
|
Facial Recognition and Modelling > Face Verification > Disguised Face Verification
|
Disguised Faces in the Wild
|
VGG-Face model features + cosine similarity metric
|
http://arxiv.org/abs/1811.08837v1
|
GAR @1% FAR
|
33.76
|
Facial Recognition and Modelling > Face Alignment
|
3DFAW
|
3D Face alignment
|
http://arxiv.org/abs/1609.09545v1
|
CVGTCE
|
3.4767%
|
Facial Recognition and Modelling > Face Alignment
|
3DFAW
|
3D Face alignment
|
http://arxiv.org/abs/1609.09545v1
|
GTE
|
4.5623
|
Facial Recognition and Modelling > Face Alignment
|
AFLW
|
SynergyNet
|
https://arxiv.org/abs/2110.09772v3
|
Mean NME
|
4.06
|
Facial Recognition and Modelling > Face Alignment
|
AFLW
|
3DDFA_V2
|
https://arxiv.org/abs/2009.09960v2
|
Mean NME
|
4.43
|
Facial Recognition and Modelling > Face Alignment
|
AFLW
|
3DDFA
|
http://arxiv.org/abs/1804.01005v1
|
Mean NME
|
4.55
|
Facial Recognition and Modelling > Face Alignment
|
LS3D-W Balanced
|
3D-FAN
|
http://arxiv.org/abs/1703.07332v3
|
AUC0.07
|
72.3%
|
Facial Recognition and Modelling > Face Alignment
|
CelebA Aligned
|
Progressive Face SR
|
https://arxiv.org/abs/1908.08239v1
|
MOS
|
3.73
|
Facial Recognition and Modelling > Face Alignment
|
CelebA Aligned
|
Progressive Face SR
|
https://arxiv.org/abs/1908.08239v1
|
MS-SSIM
|
0.902
|
Facial Recognition and Modelling > Face Alignment
|
CelebA Aligned
|
Progressive Face SR
|
https://arxiv.org/abs/1908.08239v1
|
PSNR
|
22.66
|
Facial Recognition and Modelling > Face Alignment
|
CelebA Aligned
|
Progressive Face SR
|
https://arxiv.org/abs/1908.08239v1
|
SSIM
|
0.685
|
Facial Recognition and Modelling > Face Alignment
|
WFW (Extra Data)
|
SH-FAN
|
https://arxiv.org/abs/2111.02360v1
|
NME (inter-ocular)
|
3.72
|
Facial Recognition and Modelling > Face Alignment
|
WFW (Extra Data)
|
SH-FAN
|
https://arxiv.org/abs/2111.02360v1
|
AUC@10 (inter-ocular)
|
63.1
|
Facial Recognition and Modelling > Face Alignment
|
WFW (Extra Data)
|
SH-FAN
|
https://arxiv.org/abs/2111.02360v1
|
FR@10 (inter-ocular)
|
1.55
|
Facial Recognition and Modelling > Face Alignment
|
WFW (Extra Data)
|
FaRL-B (epoch 16)
|
https://arxiv.org/abs/2112.03109v3
|
NME (inter-ocular)
|
3.96
|
Facial Recognition and Modelling > Face Alignment
|
WFW (Extra Data)
|
FaRL-B (epoch 16)
|
https://arxiv.org/abs/2112.03109v3
|
AUC@10 (inter-ocular)
|
61.16
|
Facial Recognition and Modelling > Face Alignment
|
WFW (Extra Data)
|
FaRL-B (epoch 16)
|
https://arxiv.org/abs/2112.03109v3
|
FR@10 (inter-ocular)
|
1.76
|
Facial Recognition and Modelling > Face Alignment
|
WFW (Extra Data)
|
SPIGA
|
https://arxiv.org/abs/2210.07233v1
|
NME (inter-ocular)
|
4.06
|
Facial Recognition and Modelling > Face Alignment
|
WFW (Extra Data)
|
SPIGA
|
https://arxiv.org/abs/2210.07233v1
|
AUC@10 (inter-ocular)
|
60.56
|
Facial Recognition and Modelling > Face Alignment
|
WFW (Extra Data)
|
SPIGA
|
https://arxiv.org/abs/2210.07233v1
|
FR@10 (inter-ocular)
|
2.08
|
Facial Recognition and Modelling > Face Alignment
|
WFW (Extra Data)
|
HIH
|
https://arxiv.org/abs/2104.03100v2
|
NME (inter-ocular)
|
4.08
|
Facial Recognition and Modelling > Face Alignment
|
WFW (Extra Data)
|
HIH
|
https://arxiv.org/abs/2104.03100v2
|
AUC@10 (inter-ocular)
|
60.50
|
Facial Recognition and Modelling > Face Alignment
|
WFW (Extra Data)
|
HIH
|
https://arxiv.org/abs/2104.03100v2
|
FR@10 (inter-ocular)
|
2.60
|
Facial Recognition and Modelling > Face Alignment
|
WFW (Extra Data)
|
ADNet
|
https://arxiv.org/abs/2105.10697v1
|
NME (inter-ocular)
|
4.14
|
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