# UnifiedFace: A Uniform Margin Loss Function for Face Recognition
## 4 Experiments
### Ablation Experiments
#### 4.3.2 The Comparison with State-of-the-Art Methods  
The performance comparisons of UnifiedFace with other state-of-the-art methods at different scales of training datasets are given in Tables 7 and 8, respectively. UnifiedFace outperforms other state-of-the-art methods when using small-scale datasets as training datasets. Specifically, UnifiedFace achieves state-of-the-art performance in the validation datasets LFW, CPLFW, and CFP_FP. The performance of UnifiedFace improves over the optimal loss function by 0.08%, 0.08%, and 0.04% in these three validation datasets, respectively. Competitive performance is also achieved in the remaining three validation datasets. UnifiedFace also outperforms the current popular loss function when using MS1M V3 as the training dataset. Specifically, UnifiedFace achieves state-of-the-art performance in CALFW, VGG2_FP, and CFP_FF. UnifiedFace substantially improves the performance of the model in the VGG2_FP validation dataset. Furthermore, UnifiedFace achieves suboptimal performance in all other validation sets. A comparison of UnifiedFace with popular loss functions in recent years in training sets of different sizes illustrates its excellent generalization ability.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **s** & **m** & **LFW** & **CALFW** & **CPLFW** & **VGG2\_FP** & \multicolumn{2}{c}{**CFP**} \\  & & & & & & & **CFP\_FF** & **CFP\_FP** \\ \hline SphereFace & - & 4 & 99.43\% & 91.18\% & 90.33\% & 95.46\% & 98.90\% & 96.99\% \\ SphereFaceR v1 & 64 & 1.5 & 99.63\% & 93.70\% & 92.50\% & 95.08\% & 99.46\% & 97.79\% \\ SphereFaceR v2 & 64 & 1.5 & 99.65\% & 93.37\% & 92.25\% & 95.30\% & 99.37\% & 97.50\% \\ CosFace & 64 & 0.45 & 99.63\% & 93.83\% & 92.37\% & 94.40\% & 99.37\% & 97.10\% \\ ArcFace & 64 & 0.45 & 99.62\% & 93.77\% & 92.33\% & 94.90\% & 99.40\% & 97.53\% \\ CurricularFace & 64 & 0.5 & 99.62\% & 93.90\% & 91.58\% & 94.46\% & 99.43\% & 96.90\% \\ UnifiedFace & 64 & 0.4 & 99.73\% & 93.77\% & 92.58\% & 95.36\% & 99.37\% & 97.83\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of UnifiedFace with state-of-the-art methods in using VGGFace2 as the training dataset.  
Figure 7: (**a,e**) denote the change in loss values of UnifiedFace using different training datasets with various hyperparameters, respectively. (**b,f**) indicate the comparison of loss values of UnifiedFace using different datasets with state-of-the-art methods. (**c,g**) represent the comparison of UnifiedFace using various datasets with state-of-the-art methods in the LFW validation dataset. (**d,h**) show the comparison of UnifiedFace using different datasets compared with popular methods in the CFPFP validation dataset.  
In addition, the comparison of UnifiedFace with the popular loss function in recent years in terms of loss value change during training is given. Both Figure 7b,f show that UnifiedFace has the most stable loss value change process and the smallest convergence value during the training process. Meanwhile, there is no large and drastic loss change during the training process. The accuracy comparison of UnifiedFace with popular loss functions in LFW and CFP_FP validation datasets during training is shown in Figure 7c,h, respectively.