Dental pathology is a wide field of study as it passes through several stages of diagnosis and treatment for patients. This project 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 and another model to classify the occlusion class on the 3D model. The challenges in this approach include achieving the highest possible accuracy in the reconstruction process, detecting 3D landmarks of the 3D facial model, and classifying the dental occlusion class on the 3D facial model directly.

• To reconstruct 3D facial model from 2D facial images using deep learning techniques, and work on increasing the accuracy to reach out up to 95%.

• To provide a web application for orthodontists assistance in facial analysis.

• To test the 3D facial model against the photogrammetry models to ensure the highest accuracy.

• To use the reconstructed 3D facial model for facial analysis and occlusion classification.

Face analyzer 3D system shall:

• Process a 2D facial image to check if it valid for reconstruction or not.

• Reconstruct a 3D facial model using single 2D image.

• Detect all 3D facial landmarks of the patients face.

• Output a full facial analysis report measured using detected 3D landmarks.

• Classify the class of occlusion on the reconstructed mesh.

• Develop a web-based interface for the system.