Explore FIDENTIS Analyst II, an application for the anthropological analysis of 3D scans of human faces. Focus on the functionality of 3D masks, which allow analysts to manually select anatomically significant facial regions and incorporate them into analytical processes such as facial alignment and similarity calculations.

Study existing methods for automatic detection of different anatomical parts of faces, e.g., such as eyes, nose, mouth, chin, or ears, on 3D scans. Consider both geometric and ML-based approaches. Consider detection directly from 3D triangular meshes and also from 2D images taken as snapshots from 3D data. Design and implement services for the automatic detection of selected anatomical regions.

The thesis will describe the principles of using 3D masks in FIDENTIS Analyst II and review existing solutions for the automatic detection of anatomical regions. The selection of a suitable method will be justified.

The proposed automatic detection system will be implemented and deployed as external REST services, which will be integrated into the desktop application via remote calls. The services and their deployment will be documented. The effectiveness and reliability of the detection of different facial regions will be experimentally evaluated using the provided dataset.