# Coupling RetinaFace and Depth Information to Filter False Positives
## 3 Results and Discussion
### Experiments
The effect was achieved using Gimp's foggify filter ([[https://docs.gimp.org/2.8/en/python-fu-foggify.html](https://docs.gimp.org/2.8/en/python-fu-foggify.html)]([https://docs.gimp.org/2.8/en/python-fu-foggify.html](https://docs.gimp.org/2.8/en/python-fu-foggify.html)), accessed on 30 December 2022) with turbulence = 4.0 and opacity 100. Figure 6 shows an example of perturbed images, while Table 4 shows the results of the same experiment in Table 3 applied to the perturbed images. In Figure 7 a plot of detection rate vs. number of false positives is reported (by varying the acceptance threshold) for each of the considered approaches: in the first row, results on the original dataset are shown, while the second row reports results from the fog-perturbed dataset. The usefulness of filtering is more evident when considering regions that do not contain faces. Therefore, we define a strong false positive (SFP) as a candidate face that has an ED score greater than 1. In Figure 8, a plot of the detection rate versus the number of strong false positives is presented. Figure 6: Original images (**left**), fog-perturbed images (**right**). \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Face Detector**: & \multicolumn{3}{c}{**RetinaFace**} & \multicolumn{3}{c}{**YOLOv7 Tiny**} & \multicolumn{3}{c}{**YOLOv7 Lite**} \\ \hline
**Threshold** & **Filter** & **DR** & **FP** & **DR** & **FP** & **DR** & **FP** \\ \hline \multirow{4}{*}{0.98} & none & 79.15 & 93 & 0 & 0 & 0 & 0 \\  & SIZE & 78.99 & 66 & 0 & 0 & 0 & 0 \\  & SIZE + SEC & 78.66 & 63 & 0 & 0 & 0 & 0 \\  & all & 77.36 & 59 & 0 & 0 & 0 & 0 \\ \hline \multirow{4}{*}{0.5} & none & 87.46 & 219 & 69.54 & 264 & 59.77 & 274 \\  & SIZE & 87.30 & 132 & 69.22 & 186 & 59.61 & 217 \\  & SIZE + SEC & 86.64 & 118 & 68.73 & 172 & 59.61 & 201 \\  & all & 85.34 & 101 & 67.26 & 153 & 57.98 & 189 \\ \hline \multirow{4}{*}{0.2} & none & 88.64 & 321 & 72.15 & 363 & 62.54 & 398 \\  & SIZE & 88.27 & 171 & 71.82 & 233 & 62.38 & 289 \\  & SIZE + SEC & 87.62 & 149 & 71.34 & 211 & 62.38 & 257 \\  & all & 86.32 & 123 & 69.71 & 180 & 60.59 & 234 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of face detection methods combined by filtering steps on the fog-perturbed images. Figure 7: Plot of the detection rate vs. number of false positives for the 3 face detectors (RetinaFace, YOLOv7 Tiny, YOLOv7 Lite). First row: original dataset, second row: fog-perturbed dataset. According to the plots in Figure 7 it is clear that the best trade-off between the detection rate and the number of false positives is gained by coupling the face detector with the filtering method named \"size\". It happens for all the approaches tested in this paper, but undoubtedly this is more interesting for YOLO since the performance is lower. The advantages of all the filtering approaches are more evident in the presence of strong false positives, as shown in Figure 8.  
As for the choice of basic face detector, obviously it must be dictated by the system requirements: YOLO is faster, but less accurate, while RetinaFace is very accurate but requires more resources and a longer computation time. It is important to note that the most suited application of our idea will be in AR/VR headsets, mainly in portable devices where lightweight networks will be used, at least for the next few years. Although the proposed approach has only been evaluated using a single dataset, it is believed to be effective in real-world conditions because the MERGED dataset is highly realistic. It includes many images taken in natural settings and features multiple frontal faces, not just a single one, making it more representative of real-world conditions. Moreover, the fog-perturbed version of the dataset makes the benchmark even more difficult.