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.

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