Anonymous, "KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition"
- The core part of the code is based on Insightface.
Training dataset:
- CASIA-WebFace: 0.5M images of 10k identities.
Test dataset:
- Labeled Faces in the Wild (LFW): 13k images of 5,749 identities.
- Cross-Age LFW (CALFW).
- Cross-Pose (CPLFW).
- YouTube Faces (YTF): 3,425 videos of 1,595 identities.
- Celebrities in Frontal-Prole (CFP).
- AgeDB-30: 12,240 images of 440 identities.
- IARPA Janus Benchmark: IJB-B and IJB-C.
- MegaFace: 1M images of 690k identities.
We obtained the datasets from here.
$ CUDA_VISIBLE_DEVICES="0,1,2,3" python -m torch.distributed.launch --nproc_per_node=4 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/base
Table 1 Verification results (%) on LFW, YTF, two pose benchmarks (CFP-FP and CPLFW) and two age benchmarks(AgeDB and CALFW).
| Method | LFW | CFP-FP | CPLFW | AgeDB | CALFW | YTF |
|---|---|---|---|---|---|---|
| Center Loss | 99.27 | - | 81.40 | - | 90.30 | 94.9 |
| SphereFace | 99.27 | - | 81.40 | - | 90.30 | 95.0 |
| VGGFace2 | 76.74 | - | 84.00 | - | 90.57 | - |
| UV-GAN | 99.60 | 94.05 | - | - | - | - |
| ArcFace | 99.82 | 98.27 | 92.08 | 98.15 | 95.45 | 98.0 |
| CirricularFace | 99.80 | 98.37 | 93.13 | 98.32 | 96.20 | - |
| ArcFace-SCF | 99.82 | 98.40 | 93.16 | 98.30 | 96.12 | - |
| KappaFace (memory buffer) | 99.83 | 98.69 | 93.22 | 98.47 | 96.23 | 98.0 |
| KappaFace (momentum encoder) | 99.83 | 98.60 | 93.40 | 98.35 | 96.15 | 98.0 |
Table 2 1:1 verification TAR (@FAR=$1e-4$) on the IJB-B and IJB-C datasets.
| Method | IJB-B | IJB-C |
|---|---|---|
| ResNet50+SENet50 | 80.0 | 84.1 |
| Multicolumn | 83.1 | 86.2 |
| P2SGrad | - | 92.3 |
| Adacos | - | 92.4 |
| ArcFace-VGG-R50 | 89.8 | 92.1 |
| ArcFace-MS1MV2-R100 | 94.2 | 95.6 |
| CurricularFace-MS1MV2-R100 | 94.8 | 96.1 |
| KappaFace-MS1MV2-R100 (memory buffer) | 95.1 | 96.4 |
| KappaFace-MS1MV2-R100 (momentum encoder) | 95.3 | 96.6 |
Table 3 Verification comparison with SOTA methods on MegaFace Challenge 1 using FaceScrub as the probe set.
| Method | Id | Ver |
|---|---|---|
| AdaptiveFace | 95.02 | 95.61 |
| P2SGrad | 97.25 | - |
| Adacos | 97.41 | - |
| CosFace | 97.91 | 97.91 |
| MV-AM-Softmax-a | 98.00 | 98.31 |
| ArcFace-MS1MV2-R100 | 98.35 | 98.48 |
| CurricularFace-MS1MV2-R100 | 98.71 | 98.64 |
| KappaFace-MS1MV2-R100 (memory buffer) | 98.77 | 98.91 |
| KappaFace-MS1MV2-R100 (momentum encoder) | 98.78 | 98.83 |
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