# A PreTI: A Thermal Image Dataset for Face and Nose Segmentation with Apes
## 4 Results
### Landmark Regression  
As in the previous subsection, we incorporate results using the ground truth: GT, GT\({}^{-10\%}\), and GT\({}^{+10\%}\) as inputs for the different models. Tables 3 and 4 report the results for landmark regression. The first part of the tables is dedicated to the Tifa model's result on the ground-truth face location. The landmark models are coupled with one or several face detection methods to analyze the face detection effect on landmark regression performance. The models performing best with regard to the mAP on the whole dataset (Table 3) or the test set only (Table 4) are highlighted in bold. FaceMesh, being entirely incorporated with BlazeFace, could not be tested on the ground-truth region of interest. As Table 3 shows and similar to face detection, the results on the validation and the test sets are better than in the train set. The variety of samples in the train set is higher than in the others, which encourages us to increase the size of our dataset. Surprisingly, the combination leading to the best performance is obtained with the \(\texttt{Thresh}_{29.8}\) method and the Tina model. This can be explained by the few instances where the Tifa model fails to detect the face, as reported in Figure 6. In contrast, the Thresh method, with a low threshold value, inputs a larger ROI that includes the face, and the Tina model is robust enough to segment the nose landmarks from this larger input. As depicted in Figure 5, the search for the best parameters of the combination of the BlazeFace, Threshold, and FaceMesh methods on the joint train and validation sets allows us to pick our parameters to evaluate the model on the test set. However, as also depicted in Figures 10 and 11, the observed metrics do not always agree, nor are they stable. This behavior may be attributed to the sparsity of our dataset and the temperature differences \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mAP** & **AP 50** & **AP 75** & **mIoU** \\ \hline GT\({}^{+10\%}\) & 0.7 & 1 & 1 & 0.836 \\ GT\({}^{-10\%}\) & 0.7 & 1 & 1 & 0.821 \\
**Tifa** & **0.744** & **0.980** & **0.902** & **0.868** \\ no ROI & 0 & 0 & 0 & 0.128 \\ Thresh\({}_{35.6}\) & 0 & 0 & 0 & 0.183 \\ BlazeFace & 0.007 & 0.037 & 0 & 0.398 \\ \(\texttt{Thresh}_{36.5}\) + BlazeFace & 0.136 & 0.512 & 0.025 & 0.576 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Face detection results on the ApeTI test set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Train** & **Validation** & **Test** & **Overall** \\ \hline GT + Tina & 0.940 & 1 & 0.989 & 0.965 \\
**GT\({}^{+10\%}\) + Tina** & **0.957** & **1** & **0.989** & **0.971** \\ GT\({}^{-10\%}\) + Tina & 0.926 & 0.993 & 0.995 & 0.956 \\ \hline no ROI + Tina & 0.926 & 1 & 0.987 & 0.953 \\ Thresh\({}_{29.8}\) + Tina & **0.965** & **1** & **0.989** & **0.981** \\ Tifa + Tina & 0.919 & 0.990 & 0.980 & 0.950 \\ Tifa\({}^{+10\%}\) + Tina & 0.949 & 0.999 & 0.980 & 0.968 \\ Tifa\({}^{-10\%}\) + Tina & 0.903 & 0.978 & 0.980 & 0.939 \\ Thresh\({}_{29.8}\) + Tifa + Tina & 0.919 & 0.990 & 0.952 & 0.950 \\ BlazeFace + FaceMesh & 0.312 & 0.363 & 0.336 & 0.336 \\ Thresh\({}_{36.5}\) + BlazeFace + FaceMesh & 0.557 & 0.557 & 0.566 & 0.557 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Landmark regression mAP score across the different ApeTI sets. across individual recording sessions. The range of temperatures may vary, and a slight change in parameters can suddenly affect a portion of the dataset. Nevertheless, the best performance achieved by these combined models, as measured by the mAP with OKS, is obtained using a threshold of 36.5 \({}^{\circ}\)C and minimum and maximum calibration values of 17 \({}^{\circ}\)C and 39 \({}^{\circ}\)C, respectively. Such a threshold may not guarantee that the entire face, which typically has a temperature between 30 \({}^{\circ}\)C and 38 \({}^{\circ}\)C, is included in the ROI inputted to BlazeFace.