# Coupling RetinaFace and Depth Information to Filter False Positives
## 3 Results and Discussion
### Experiments  
The goal of the first experiment (Table 2) was to compare the detection rates of RetinaFace ([[https://github.com/elliottzheng/face-detection](https://github.com/elliottzheng/face-detection)]([https://github.com/elliottzheng/face-detection](https://github.com/elliottzheng/face-detection)), accessed on 30 December 2022) and YOLO by adjusting the sensitivity threshold (on the score output of the detector). It will be observed that setting the threshold for increasing the detection rate generates more false positives. The results obtained by RetinaFace are clearly better than previous face detectors based on handcrafted methods or shallow neural networks (see [1] for details). RetinaFace can still be considered a state-of-the-art face detector. The second experiment aims to assess the performance of the filtering steps and their combinations. Table 3 presents the results of combining the three face detectors with the filtering steps. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Face** & \multicolumn{2}{c}{**RetinaFace**} & \multicolumn{2}{c}{**YOLOv7 Tiny**} & \multicolumn{2}{c}{**YOLOv7 Lite**} \\
**Detector:** & & & & & & \\ \hline
**Threshold** & **DR** & **FP** & **DR** & **FP** & **DR** & **FP** \\ \hline
0.02 & 95.93 & 1152 & 83.06 & 909 & 73.94 & 1491 \\
0.2 & 95.93 & 281 & 82.74 & 405 & 73.78 & 427 \\
0.5 & 95.93 & 227 & 82.41 & 309 & 73.45 & 305 \\
0.9 & 95.60 & 171 & 0.16 & 0 & 0 & 0 \\
0.98 & 94.79 & 119 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance (detection rate and false positives) of RetinaFace and YOLOv7. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Face Detector:** & \multicolumn{2}{c}{**RetinaFace**} & \multicolumn{2}{c}{**YOLOv7 Tiny**} & \multicolumn{2}{c}{**YOLOv7 Lite**} \\ \hline
**Threshold** & **Filter** & **DR** & **FP** & **DR** & **FP** & **DR** & **FP** \\ \hline \multirow{3}{*}{0.98} & None & 94.79 & 119 & 0 & 0 & 0 & 0 \\  & SIZE & 94.63 & 84 & 0 & 0 & 0 & 0 \\  & SIZE + SEC & 94.14 & 75 & 0 & 0 & 0 & 0 \\  & all & 92.67 & 71 & 0 & 0 & 0 & 0 \\ \hline \multirow{3}{*}{0.5} & None & 95.93 & 227 & 82.41 & 309 & 73.45 & 305 \\  & SIZE & 95.77 & 111 & 82.08 & 199 & 73.29 & 230 \\  & SIZE + SEC & 95.11 & 95 & 81.60 & 180 & 72.96 & 213 \\  & all & 93.65 & 85 & 79.80 & 162 & 71.01 & 194 \\ \hline \multirow{3}{*}{0.2} & None & 95.93 & 281 & 82.74 & 405 & 73.78 & 427 \\  & SIZE & 95.77 & 128 & 82.41 & 229 & 73.62 & 283 \\ \cline{1-1}  & SIZE + SEC & 95.11 & 109 & 81.92 & 206 & 73.29 & 254 \\ \cline{1-1}  & all & 93.65 & 94 & 80.13 & 179 & 71.34 & 220 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance of face detection methods combined by filtering steps. It should be noted that the complexity of the three face detectors is very different: YOLOv7 lite has a storage requirement of 3 MB, YOLOv7 tiny has a requirement of 31 MB, and RetinaFace (with ResNet backbone) requires 107 MB. The detection time of YOLOv7 tiny is 38% higher than YOLOv7 lite, and RetinaFace is 243% higher than YOLOv7 lite. The following conclusions can be drawn from the above table:  
* The best trade-off of DR and FP is obtained by RetinaFace (0.5) with the SIZE filter applied. Clearly, SIZE increases the effectiveness of RetinaFace on the test set;
* The other filters reduce the number of FP but also decrease DR;
* SIZE permits to reduce FP without a considerable reduction in DR, even when coupled with YOLOv7. Obviously, the advantage of using depth map information is more pronounced when the effectiveness of the visual sensor is reduced due to darkness, fog, or other reasons that prevent a perfect view of the scene. To highlight this advantage, an additional test was performed in which the visual spectrum images were subjected to a fog filter.