Automatic Keypoint Detection on 3D Faces Using a Dictionary of Local Shapes

Clement Creusot, Nick Pears, Jim Austin
2011 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission  
Keypoints on 3D surfaces are points that can be extracted repeatably over a wide range of 3D imaging conditions. They are used in many 3D shape processing applications; for example, to establish a set of initial correspondences across a pair of surfaces to be matched. Typically, keypoints are extracted using extremal values of a function over the 3D surface, such as the descriptor map for Gaussian curvature. That approach works well for salient points, such as the nosetip, but can not be used
more » ... t can not be used with other less pronounced local shapes. In this paper, we present an automatic method to detect keypoints on 3D faces, where these keypoints are locally similar to a set of previously learnt shapes, constituting a 'local shape dictionary'. The local shapes are learnt at a set of 14 manuallyplaced landmark positions on the human face. Local shapes are characterised by a set of 10 shape descriptors computed over a range of scales. For each landmark, the proportion of face meshes that have an associated keypoint detection is used as a performance indicator. Repeatability of the extracted keypoints is measured across the FRGC v2 database.
doi:10.1109/3dimpvt.2011.33 dblp:conf/3dim/CreusotPA11 fatcat:ivxfegkqqvbhta6tyjyjqhenj4