Facial and mandibular landmark tracking with habitual head posture estimation using linear and fiducial markers

Abstract

This study compared the accuracy of facial landmark measurements using deep learning‐based fiducial marker (FM) and arbitrary width reference (AWR) approaches. It quantitatively analysed mandibular hard and soft tissue lateral excursions and head tilting from consumer camera footage of 37 participants. A custom deep learning system recognised facial landmarks for measuring head tilt and mandibular lateral excursions. Circular fiducial markers (FM) and inter‐zygion measurements (AWR) were validated against physical measurements using electrognathography and electronic rulers. Results showed notable differences in lower and mid‐face estimations for both FM and AWR compared to physical measurements. The study also demonstrated the comparability of both approaches in assessing lateral movement, though fiducial markers exhibited variability in mid‐face and lower face parameter assessments. Regardless of the technique applied, hard tissue movement was typically seen to be 30% less than soft tissue among the participants. Additionally, a significant number of participants consistently displayed a 5 to 10° head tilt.

Predictive modelling of freeway space utilising clinical history, normalised muscle activity, dental occlusion, and mandibular movement analysis

“…The video recordings were processed using a deep learning-based facial landmark tracking system to assess habitual head tilting patterns and soft tissue displacements during lateral excursions and speech, based on previous research implementations 11 . This was accomplished with a set of open-source in-house software developed by the authors, namely Dental Loop FLT 12 v5.2 ( https://github.com/ElsevierSoftwareX/SOFTX-D-23-00353 ) and Dental Loop SnP v1.0 (…”