3D Morphable Models are valuable tools in face perception research. Their construction requires vertex-to-vertex correspondence for all face scans in a database. At ZIB, we are developing face matching methods to establish highly accurate dense correspondence in fully automatic fashion. The goal of this project is to improve face matching by training a convolutional neural network to correspondingly label the vertices of facial meshes.

Please see the attachment for further details.

 

 

Research Group: 
Attachements: 
Visualization Techniques for 3D Image Data
MA-05/19
3D Convolutional Neural Networks for Classification of Diseases
MA-04/19
Evaluation and Comparison of Linear and Non-linear Approaches for the Analysis and Synthesis of Facial Motion
MA-01/19