Year | Publication | DCNN architecture | Loss function | Train data | LFW (%) |
---|---|---|---|---|---|
2014 | DeepFace [27] | 9 layer deep CNN | Softmax | Private dataset | 97.35 |
2014 | DeepId [54] | 9 layer deep CNN | Softmax | CelebFaces+ | 97.45 |
2014 | DeepID2 [55] | 9 layer deep CNN | Softmax, contrastive | CelebFaces [106] | 99.15 |
2014 | DeepID2+ [56] | 9 layer deep CNN | Softmax, contrastive | CelebFaces+, | 99.47 |
WDRef [129] | 99.53 | ||||
2015 | VGGFace [37] | VGGNet [35] | Softmax, triplet | VGGFace [37] | 98.95 |
2015 | DeepID3 [28] | GoogleNet [36], | Softmax, contrastive | CelebFaces+, | 99.53 |
VGGNet [35] | WDRef [129] | 99.53 | |||
2015 | FaceNet [29] | GoogleNet [36], | Triplet | Private dataset [29] | 99.63 |
Zeiler_Fergus [125] | |||||
2015 | Baidu [57] | DCNN with 9 convolutions | Triplet | Private dataset | 99.77 |
2018 | SphereFace [60] | ResNet-64 [70] | SphereFace [60] | CASIA-WebFace [88] | 99.42 |
2018 | CosFace [61] | ResNet-64 [70] | CosFace [61] | CASIA-WebFace [88] | 99.33 |
Private dataset | 99.73 | ||||
2019 | ArcFace [38] | ResNet-100 [70] | ArcFace [38] | ms1m | 99.83 |
Open Source Implementations | |||||
- | DLIB library | ResNet-34 [70] | Triplet | VGGFace [37], | 99.38 |
FaceScrub [108] | |||||
2016 | OpenFace [65] | GoogleNet [36] | Triplet | CASIA-WebFace [88], | 92.92 |
FaceScrub [108] | |||||
- | FaceNet_Re [66] | Inception-ResNet-v1 [71] | Softmax | VGGFace2 [78] | 99.65 |
Inception-ResNet-v1 [71] | Softmax | CASIA-WebFace [88] | 99.05 | ||
Inception-ResNet-v1 [71] | Centre [79] | VGGFace2 [78] | 99.2 |