Face analyzer 3D: Automatic facial profile detection and occlusion classification for dental purposes

Abstract

Dental pathology is a wide field of study as it passes through several stages of diagnosis and treatment for patients. This paper aims to assist orthodontists in classifying dental occlusion and measuring the asymmetry caused by it. The system takes a 2D facial image as input and uses it to reconstruct the 3D model. As 3D models have a lower error rate in information loss, they are more accurate than 2D images. Then, it uses a deep learning model to detect 3D facial landmarks on a 2D image to measure facial asymmetry. The challenges in this approach include achieving the highest possible accuracy in the reconstruction process and detecting 3D landmarks on the 3D facial model. The used technique in reconstruction reaches up to 90% accuracy compared to photogrammetry techniques. The proposed framework is expected to be time-efficient and to achieve up to 89% accuracy in the analysis and classification.