task_path
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⌀ | dataset
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⌀ | model_name
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⌀ |
|---|---|---|---|---|---|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
HeadSharing: SH-KD
|
https://arxiv.org/abs/2201.06945v2
|
TAR @ FAR=1e-5
|
93.73%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
HeadSharing: SH-KD
|
https://arxiv.org/abs/2201.06945v2
|
TAR @ FAR=1e-6
|
90.24%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
HeadSharing: SH-KD
|
https://arxiv.org/abs/2201.06945v2
|
training dataset
|
MS1M V3
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
HeadSharing: SH-KD
|
https://arxiv.org/abs/2201.06945v2
|
model
|
MobileFaceNet
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
HeadSharing: TH-KD
|
https://arxiv.org/abs/2201.06945v2
|
TAR @ FAR=1e-4
|
95.48%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
HeadSharing: TH-KD
|
https://arxiv.org/abs/2201.06945v2
|
TAR @ FAR=1e-5
|
93.50%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
HeadSharing: TH-KD
|
https://arxiv.org/abs/2201.06945v2
|
TAR @ FAR=1e-6
|
89.82%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
HeadSharing: TH-KD
|
https://arxiv.org/abs/2201.06945v2
|
training dataset
|
MS1M V3
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
HeadSharing: TH-KD
|
https://arxiv.org/abs/2201.06945v2
|
model
|
MobileFaceNet
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
ArcFace+CSFM
|
https://arxiv.org/abs/2207.10180v1
|
TAR @ FAR=1e-4
|
95.9%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
ArcFace+CSFM
|
https://arxiv.org/abs/2207.10180v1
|
TAR @ FAR=1e-5
|
94.06%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
ArcFace+CSFM
|
https://arxiv.org/abs/2207.10180v1
|
TAR @ FAR=1e-6
|
89.34%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
ArcFace+CSFM
|
https://arxiv.org/abs/2207.10180v1
|
Rank-1
|
96.31
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
ArcFace+CSFM
|
https://arxiv.org/abs/2207.10180v1
|
Rank-5
|
97.48
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
Partial FC
|
https://arxiv.org/abs/2203.15565v1
|
TAR @ FAR=1e-4
|
98.00%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
Partial FC
|
https://arxiv.org/abs/2203.15565v1
|
TAR @ FAR=1e-5
|
97.23%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
Partial FC
|
https://arxiv.org/abs/2203.15565v1
|
training dataset
|
WebFace42M
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
Partial FC
|
https://arxiv.org/abs/2203.15565v1
|
model
|
ViT-L
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
PartialFC
|
https://arxiv.org/abs/2203.15565v1
|
TAR @ FAR=1e-4
|
97.97%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
PartialFC
|
https://arxiv.org/abs/2203.15565v1
|
TAR @ FAR=1e-5
|
96.93%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
PartialFC
|
https://arxiv.org/abs/2203.15565v1
|
training dataset
|
WebFace42M
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
PartialFC
|
https://arxiv.org/abs/2203.15565v1
|
model
|
R200
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
ArcFace
|
https://arxiv.org/abs/1801.07698v4
|
TAR @ FAR=1e-5
|
96.07%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
ArcFace
|
https://arxiv.org/abs/1801.07698v4
|
training dataset
|
IBUG-500K
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
ArcFace
|
https://arxiv.org/abs/1801.07698v4
|
model
|
R100
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
Mag+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR @ FAR=1e-5
|
94.7%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
Cos+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR @ FAR=1e-3
|
97.57
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
Cos+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR @ FAR=1e-4
|
96.38%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
Cos+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR @ FAR=1e-5
|
94.47%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
Cos+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
training dataset
|
MS1MV2
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
Cos+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
model
|
R100
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
L2E+IS-sampling
|
https://ieeexplore.ieee.org/document/9607686
|
TAR @ FAR=1e-3
|
97.05%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
L2E+IS-sampling
|
https://ieeexplore.ieee.org/document/9607686
|
TAR @ FAR=1e-4
|
95.49%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
L2E+IS-sampling
|
https://ieeexplore.ieee.org/document/9607686
|
TAR @ FAR=1e-5
|
93.25%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
L2E+IS-sampling
|
https://ieeexplore.ieee.org/document/9607686
|
training dataset
|
MS1M V3
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
L2E+IS-sampling
|
https://ieeexplore.ieee.org/document/9607686
|
model
|
MobileFaceNet
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
MagFace++
|
https://arxiv.org/abs/2103.06627v4
|
TAR @ FAR=1e-4
|
95.97%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
MagFace++
|
https://arxiv.org/abs/2103.06627v4
|
TAR @ FAR=1e-5
|
90.36%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
MagFace++
|
https://arxiv.org/abs/2103.06627v4
|
training dataset
|
MS1MV2
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
MagFace++
|
https://arxiv.org/abs/2103.06627v4
|
model
|
R100
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
circle loss
|
https://arxiv.org/abs/2002.10857v2
|
TAR @ FAR=1e-3
|
96.29%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
circle loss
|
https://arxiv.org/abs/2002.10857v2
|
TAR @ FAR=1e-4
|
93.95%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
circle loss
|
https://arxiv.org/abs/2002.10857v2
|
TAR @ FAR=1e-5
|
89.60%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
circle loss
|
https://arxiv.org/abs/2002.10857v2
|
training dataset
|
MS1M Cleaned
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
circle loss
|
https://arxiv.org/abs/2002.10857v2
|
model
|
R100
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
WebFace42M baseline
|
https://arxiv.org/abs/2103.04098v1
|
TAR @ FAR=1e-4
|
97.7%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
WebFace42M baseline
|
https://arxiv.org/abs/2103.04098v1
|
training dataset
|
WebFace42M
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
WebFace42M baseline
|
https://arxiv.org/abs/2103.04098v1
|
model
|
R100
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
AdaFace (WebFace4M)
|
https://arxiv.org/abs/2204.00964v2
|
TAR @ FAR=1e-4
|
97.39%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
FFC
|
https://arxiv.org/abs/2105.10375v5
|
TAR @ FAR=1e-4
|
97.31%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
FFC
|
https://arxiv.org/abs/2105.10375v5
|
training dataset
|
WebFace42M
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
FFC
|
https://arxiv.org/abs/2105.10375v5
|
model
|
R100
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
CAFace+AdaFace (WebFace4M)
|
https://arxiv.org/abs/2210.10864v3
|
TAR @ FAR=1e-3
|
98.08
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
CAFace+AdaFace (WebFace4M)
|
https://arxiv.org/abs/2210.10864v3
|
TAR @ FAR=1e-4
|
97.3%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
AdaFace (MS1MV3)
|
https://arxiv.org/abs/2204.00964v2
|
TAR @ FAR=1e-4
|
97.09%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
AdaFace (MS1MV2)
|
https://arxiv.org/abs/2204.00964v2
|
TAR @ FAR=1e-4
|
96.89%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
ElasticFace-Cos
|
https://arxiv.org/abs/2109.09416v4
|
TAR @ FAR=1e-4
|
96.57%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
ElasticFace-Cos
|
https://arxiv.org/abs/2109.09416v4
|
training dataset
|
MS1M V2
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
ElasticFace-Cos
|
https://arxiv.org/abs/2109.09416v4
|
model
|
R100
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
Arc+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR @ FAR=1e-3
|
97.51
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
Arc+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
TAR @ FAR=1e-4
|
96.33%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
QMagFace
|
https://arxiv.org/abs/2111.13475v3
|
TAR @ FAR=1e-2
|
98.51
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
QMagFace
|
https://arxiv.org/abs/2111.13475v3
|
TAR @ FAR=1e-3
|
97.62
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
QMagFace
|
https://arxiv.org/abs/2111.13475v3
|
TAR @ FAR=1e-4
|
96.19%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
CurricularFace
|
https://arxiv.org/abs/2004.00288v1
|
TAR @ FAR=1e-4
|
96.1%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
PFEfuse + match
|
https://arxiv.org/abs/1904.09658v4
|
TAR @ FAR=1e-2
|
97.17%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
PFEfuse + match
|
https://arxiv.org/abs/1904.09658v4
|
TAR @ FAR=1e-3
|
95.49%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
PFEfuse + match
|
https://arxiv.org/abs/1904.09658v4
|
training dataset
|
MS1M V2
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
PFEfuse + match
|
https://arxiv.org/abs/1904.09658v4
|
model
|
SphereFace64
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
VGGFace2_ft
|
http://arxiv.org/abs/1710.08092v2
|
TAR @ FAR=1e-2
|
96.7%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
VGGFace2_ft
|
http://arxiv.org/abs/1710.08092v2
|
TAR @ FAR=1e-3
|
92.7%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
VGGFace2_ft
|
http://arxiv.org/abs/1710.08092v2
|
training dataset
|
Vggface2
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
VGGFace2_ft
|
http://arxiv.org/abs/1710.08092v2
|
model
|
R50
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
AIM
|
http://arxiv.org/abs/1809.00338v2
|
TAR @ FAR=1e-2
|
93.5%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
MN-vc
|
http://arxiv.org/abs/1807.09192v1
|
TAR @ FAR=1e-2
|
92.70%
|
Facial Recognition and Modelling > Face Verification
|
IJB-C
|
FaceNet
|
http://arxiv.org/abs/1503.03832v3
|
TAR @ FAR=1e-2
|
66.5%
|
Facial Recognition and Modelling > Face Verification
|
YouTube Faces DB
|
SeqFace, 1 ResNet-64
|
http://arxiv.org/abs/1803.06524v2
|
Accuracy
|
98.12%
|
Facial Recognition and Modelling > Face Verification
|
YouTube Faces DB
|
ArcFace + MS1MV2 + R100,
|
https://arxiv.org/abs/1801.07698v4
|
Accuracy
|
98.02%
|
Facial Recognition and Modelling > Face Verification
|
YouTube Faces DB
|
CosFace
|
http://arxiv.org/abs/1801.09414v2
|
Accuracy
|
97.6%
|
Facial Recognition and Modelling > Face Verification
|
YouTube Faces DB
|
VGG-Face
|
https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/
|
Accuracy
|
97.40%
|
Facial Recognition and Modelling > Face Verification
|
YouTube Faces DB
|
PFEfuse+match
|
https://arxiv.org/abs/1904.09658v4
|
Accuracy
|
97.36%
|
Facial Recognition and Modelling > Face Verification
|
YouTube Faces DB
|
QAN
|
http://arxiv.org/abs/1704.03373v1
|
Accuracy
|
96.17%
|
Facial Recognition and Modelling > Face Verification
|
YouTube Faces DB
|
Light CNN-29
|
http://arxiv.org/abs/1511.02683v4
|
Accuracy
|
95.54%
|
Facial Recognition and Modelling > Face Verification
|
YouTube Faces DB
|
Git Loss
|
http://arxiv.org/abs/1807.08512v4
|
Accuracy
|
95.30%
|
Facial Recognition and Modelling > Face Verification
|
YouTube Faces DB
|
FaceNet
|
http://arxiv.org/abs/1503.03832v3
|
Accuracy
|
95.12%
|
Facial Recognition and Modelling > Face Verification
|
YouTube Faces DB
|
SphereFace
|
http://arxiv.org/abs/1704.08063v4
|
Accuracy
|
95.0%
|
Facial Recognition and Modelling > Face Verification
|
YouTube Faces DB
|
DeepId2+
|
https://arxiv.org/abs/1412.1265v1
|
Accuracy
|
93.2%
|
Facial Recognition and Modelling > Face Verification
|
YouTube Faces DB
|
3DMM face shape parameters + CNN
|
http://arxiv.org/abs/1612.04904v1
|
Accuracy
|
88.80%
|
Facial Recognition and Modelling > Face Verification
|
Oulu-CASIA NIR-VIS
|
LightCNN-29 + DVG
|
https://arxiv.org/abs/1903.10203v3
|
TAR @ FAR=0.001
|
92.9
|
Facial Recognition and Modelling > Face Verification
|
Oulu-CASIA NIR-VIS
|
LightCNN-29 + DVG
|
https://arxiv.org/abs/1903.10203v3
|
TAR @ FAR=0.01
|
98.5
|
Facial Recognition and Modelling > Face Verification
|
Oulu-CASIA NIR-VIS
|
DVR Wu et al. (2019)
|
http://arxiv.org/abs/1809.01936v3
|
TAR @ FAR=0.001
|
84.9
|
Facial Recognition and Modelling > Face Verification
|
Oulu-CASIA NIR-VIS
|
DVR Wu et al. (2019)
|
http://arxiv.org/abs/1809.01936v3
|
TAR @ FAR=0.01
|
97.2
|
Facial Recognition and Modelling > Face Verification
|
Oulu-CASIA NIR-VIS
|
W-CNN He et al. (2018)
|
http://arxiv.org/abs/1708.02412v1
|
TAR @ FAR=0.001
|
54.6
|
Facial Recognition and Modelling > Face Verification
|
Oulu-CASIA NIR-VIS
|
W-CNN He et al. (2018)
|
http://arxiv.org/abs/1708.02412v1
|
TAR @ FAR=0.01
|
81.5
|
Facial Recognition and Modelling > Face Verification
|
MegaFace
|
Prodpoly
|
https://arxiv.org/abs/2006.13026v2
|
Accuracy
|
98.95%
|
Facial Recognition and Modelling > Face Verification
|
MegaFace
|
ElasticFace-Arc
|
https://arxiv.org/abs/2109.09416v4
|
Accuracy
|
98.81%
|
Facial Recognition and Modelling > Face Verification
|
MegaFace
|
GhostFaceNetV2-1
|
https://ieeexplore.ieee.org/document/10098610
|
Accuracy
|
98.72%
|
Facial Recognition and Modelling > Face Verification
|
MegaFace
|
ArcFace + MS1MV2 + R100 + R
|
https://arxiv.org/abs/1801.07698v4
|
Accuracy
|
98.48%
|
Facial Recognition and Modelling > Face Verification
|
MegaFace
|
DiscFace
|
https://openaccess.thecvf.com/content/ACCV2020/html/Kim_DiscFace_Minimum_Discrepancy_Learning_for_Deep_Face_Recognition_ACCV_2020_paper.html
|
Accuracy
|
97.44%
|
Facial Recognition and Modelling > Face Verification
|
MegaFace
|
Dynamic AdaCos
|
https://arxiv.org/abs/1905.00292v2
|
Accuracy
|
97.41%
|
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