# Small-Scale Face Detection Based on Improved R-FCN
## 6 Experiments
### Effects of RFAB
#### 6.3.2 Comparison of AP between the Method Proposed and the Classical Methods  
Table 5 shows AP of the proposed method and several typical methods on the Wider Face verification set. AP of the several typical methods are from the official website of the Wider Face dataset. Table 5 shows that AP of the proposed method is higher than that of the typical methods. Although the Hard subset of Wider Face has several small-scale faces, the proposed method is still higher than comparative methods on the Hard subset. Therefore, the proposed method is more robust for the small-scale faces than other methods.  
Figure 8: The effect diagram of face detection based on the proposed method. we use the same image to test the effect of R-FCN and the proposed method on small-scale faces. In the same image, the green boxes are the faces detected by R-FCN, and the red boxes are the face detected by the proposed method.  
3.3 Comparison of Precision-Recall (PR) Curves between the Proposed Method and the Classical Methods  
Figure 9 shows the PR curves of the proposed method and other classical methods on Wider Face. As shown in the figure, the PR curve of the proposed method is closer to the right side than that of other methods. Under the same recall rate, the precision of the proposed method is the highest, which shows that the proposed method is superior to the classical methods of comparison. The reason is that the proposed method adds the feature fusion branch and RFAB to improve feature discrimination.  
3.4 Comparison of the Proposed Method and Classical Methods on Face Detection Dataset and Benchmark (FDDB)  
Figure 10 shows the discrete receiver operating characteristic (ROC) curves of the proposed method on the FDDB dataset. Two evaluation criteria of FDDB dataset are considered: discROC and contROC curve. For discROC, if IoU of prediction box and the ground truth is greater than 0.5,  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **Easy** & **Medium** & **Hard** \\ \hline Multi-scale Cascade CNN [29] & 0.691 & 0.664 & 0.424 \\ Locally decorrelated channel features & 0.790 & 0.769 & 0.522 \\ (LDCF+) [31] & 0.848 & 0.825 & 0.598 \\ SulfateFace [32] & 0.868 & 0.867 & 0.772 \\ Contextual Multi-Scale Region-based CNN & 0.899 & 0.874 & 0.624 \\ (CMS-RCNN) [33] & 0.913 & 0.896 & 0.794 \\ \hline \hline \end{tabular}
\end{table}
Table 5: AP comparison between the proposed method and typical face detection methods.  
Figure 9: PR curves of the proposed method and typical face detection methods.  
then it will be judged as a true positive sample, while contROC must be calculated by weighting IoU. The proposed method uses an unlimited training method. Thus, it is first trained with the Wider Face dataset and then tested on FDDB. The detector uses rectangles to mark faces, while FDDB uses ellipses to mark faces. The true positive rate of the proposed method in contROC will be lower than that in discROC.  
Figure 10 shows that, if discROC is taken as the evaluation criteria, then the performance of the proposed method is better than that of multitask cascade CNN [9] and LDCF + [31] but is slightly worse than that of ScaleFace [32]. This result is due to the fact that the proposed method maps all RoIs to the feature layer of the same depth, while Scaleface uses different networks to detect RoIs of a specific scale. If contROC is used as the evaluation criteria, then the performance of the proposed method is better than that of ScaleFace and LDCF + but is worse than that of multitask cascade CNN. The reason is that multitask cascade CNN uses three cascaded networks to continuously fine tune the position of the prediction box. Thus, the final prediction results are relatively higher.  
In conclusion, the results of different methods under distinct evaluation standards vary. Compared with those of Wider Face, the small-scale face samples of FDDB are limited. Table 5 and Figures 9 and 10 show that our method has a better comprehensive performance for small-scale face detection than the other methods.