# UnifiedFace: A Uniform Margin Loss Function for Face Recognition
## 4 Experiments
### Experiments in GhostFaceNet  
We report on the performance of GhostFaceNet trained using UnifiedFace., as shown in Table 9. Simultaneously, the performance comparison of UnifiedFace with popular loss functions in lightweight networks is given.  
The popular loss functions of recent years are used to train GhostFaceNet and compare it with UnifiedFace. The margins of SphereFace, CosFace, and AirFace [55] are set to 4, 0.3, and 0.45, respectively. AirFace is a loss function specifically designed for lightweight network models, and its linear target logit makes the training process very stable. The margins of ArcFace are given as 0.15, 0.30, and 0.35, and are pre-trained by softmax before using ArcFace. On LFW and CFP_FP, UnifiedFace is better than the other loss functions. In particular, UnifiedFace substantially improves the performance of the model in the cross-pose verification set CFP_FP. Furthermore, in CALFW, UnifiedFace achieves similarly competitive results. The experimental results also demonstrate the degradation of model performance caused by excessive margins in lightweight networks when using margin-based loss functions for supervised training. For example, the performance for the case of m = 0.45 is lower than that of m = 0.35 in UnifiedFace. Likewise, the performance in ArcFace is similar. The fact that UnifiedFace still achieves optimal performance in lightweight networks despite the introduction of large margins illustrates the advantage of introducing both additive and multiplicative margins in UnifiedFace.  
\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 & - & 3 & 99.80\% & 96.00\% & 92.42\% & 95.34\% & 99.73\% & 97.57\% \\ SphereFaceR v1 & 64 & 1.5 & 99.78\% & 96.03\% & 92.55\% & 95.04\% & 99.78\% & 97.39\% \\ SphereFaceR v2 & 64 & 1.5 & 99.82\% & 96.03\% & 92.97\% & 95.69\% & 99.77\% & 98.00\% \\ CosFace & 64 & 0.45 & 99.82\% & 96.08\% & 92.27\% & 94.38\% & 99.76\% & 96.89\% \\ ArcFace & 64 & 0.45 & 99.77\% & 96.12\% & 92.43\% & 94.66\% & 99.76\% & 97.13\% \\ CurricularFace & 64 & 0.5 & 99.80\% & 96.02\% & 92.05\% & 94.06\% & 99.77\% & 96.77\% \\ UnifiedFace & 50 & 0.4 & 99.80\% & 96.17\% & 92.65\% & 96.52\% & 99.79\% & 97.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison of UnifiedFace with state-of-the-art methods in using MS1M V3 as the training dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loss** & **m** & **LFW** & **CALFW** & **CFP-FP** \\ \hline SphereFace & 4 & 99.47\% & 94.97\% & 91.79\% \\ CosFace & 0.3 & 99.35\% & 94.27\% & 92.23\% \\ ArcFace & 0.15 & 99.53\% & 94.58\% & 94.21\% \\ ArcFace & 0.3 & 99.52\% & 94.63\% & 93.46\% \\ ArcFace & 0.35 & 99.50\% & 94.67\% & 93.10\% \\ AirFace & 0.45 & 99.48\% & 94.98\% & 92.73\% \\ UnifiedFace & 0.45 & 99.60\% & 94.53\% & 92.74\% \\ UnifiedFace & 0.4 & 99.63\% & 94.80\% & 93.70\% \\ UnifiedFace & 0.35 & 99.50\% & 94.63\% & 94.54\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance comparison between UnifiedFace and popular loss functions in lightweight networks.