## 4 Discussion  
The present study aimed to investigate the reconstruction of the external surface of the face by using CBCT and extraoral photographs of the patient. Regarding the rendering of the CBCT volume processing, it was verified that the 3D representation was realistic to the patient's characteristic dimensions and proportions. Despite not having developed a method to measure its accuracy, it was found that the data present in the DICOM volumes can be used together with photos to reconstruct the skin surface of the subject. The thresholding technique that was developed showed good segmentation and processing capabilities for all the subjects in the database. It is a simple and fast algorithm as opposed to machine learning techniques or the implementation of neural networks, which would require more computational power. To export the final model, various options were considered (OBJ, PLY and STL); however the PNG format requires less memory and is the easiest to manipulate as it does not require specialized software for three-dimensional models. The low contrast of soft tissues in CBCT makes the segmentation of the skin complex; however, this problem was successfully managed. A downside of the methodology studied is the possibility that CBCT may contain metallic objects that serve as obstacles and prevent the complete representation of the patient, such as the presence of chin and head rests. As a solution, it is necessary to ensure that the CBCT is acquired with the maximum possible exposure of the patient's head. The quality of smoothing of the model surface and noise removal are parameters that were only evaluated from a user perspective. As for the image processing and fusion of 2D and 3D modalities, the fusion of DICOM images from CBCT with portrait photographs was successfully achieved. The face recognition algorithm is faster to execute when compared to the face-swapping process. However, it presents limitations when it comes to its success rate at identifying all 68 fiducial points. The identification of the 68 fiducial points is not an obstacle for portrait photographs, but it becomes a challenge when it comes to the processing of 3D model frames as it is necessary that all locations corresponding to the 68-point map be clearly visible. On the other hand, this algorithm was not optimized to the recognition of faces in three-dimensional models, such as the ones used, because this presented less contrast. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **Case 1** & **Case 2** & **Case 4** & **Case 3** & **Median** & **Coefficient** & **Median** & **Coefficient** \\  & & & **(Decoy)** & **Median** & **Variation** & **(No Decoy)** & **Variation** \\ A & Photo-realism & 6.429 & 5.43 & 6.571 & 4.86 & 5.821 & 68\% & 6.143 & 48\% \\  & Rendering & 6.857 & 6.00 & 6.714 & 5.00 & 6.143 & 64\% & 6.524 & 35\% \\  & quality & 6.571 & 5.86 & 6.286 & 6.71 & 6.357 & 29\% & 6.238 & 25\% \\ B & Photo-realism & 5.714 & 5.57 & 6.000 & 4.86 & 5.536 & 34\% & 5.762 & 16\% \\  & quality & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the questionnaires (scale of 1 to 10, where 1 indicates the minimum value and 10 the maximum value). The quadratic adjustment was adequate for the X coordinates, but the adjustment of the Y coordinates was less successful. Thus, it can be concluded that, according to the results obtained, the adjustment in the ordinates were of a lower quality. Overall, the method for mapping faces onto three-dimensional models proved to be simpler and easier to process, with the disadvantage of a limited movement of the patient's head model. Lastly, the results of the questionnaires that were filled in by the orthodontists revealed that case 3, referred to as the decoy, performed its function well. Not only was the mean result lower when case 3 was considered, but also the mean deviation was significantly higher, which indicates that the confidence margin in the results was low.