Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods. X Li, J Liu, J Baron et al.

3dMD Customer Research, Computer Science | Computer Vision

The workflow… we first performed facial geometry capture with a 3dMD system; imported into landmarker.io to annotate each face manually to achieve ground-truth; rasterized each face at 49 angles and 6 focal lengths; calculated the ground-truth 2D landmark locations; and analyzed performance of each method by calculating NRMSE error between a method’s predicted landmarks and the 2D ground-truth locations.

Automatic Assessment of 3-Dimensional Facial Soft Tissue Symmetry Before and After Orthognathic Surgery Using a Machine Learning Model. LJ Lo, CT Yang, CT Ho, CH Liao, HH Lin.

3dMD Customer Research, Computer Science | Computer Vision, Medical, 3dMDface, Craniofacial/Cleft, Plastics

This study applied the transfer learning model with a convolutional neural network based on 3-dimensional (3D) contour line features to evaluate the facial symmetry before and after OGS. A total of 158 patients were recruited in a retrospective cohort study for the assessment and comparison of facial symmetry before and after OGS from January 2018 to March 2020. Three-dimensional facial photographs were captured by the 3dMD face system in a natural head position, with eyes looking forward, relaxed facial muscles, and habitual dental occlusion before and at least 6 months after surgery.