A Machine-Learning Approach to Keypoint Detection and Landmarking on 3D Meshes
International Journal of Computer Vision
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Facial symmetry is one of the number of aesthetic traits associated with health, physical attractiveness and beauty of a person. In this paper, we present a novel method for enhancing the symmetry of a scanned 3D face automatically. To handle detailed areas of the face, we developed a new local 3D shape descriptor based on facial features and surface curvatures. Our shape descriptor can improve the accuracy when deforming a 3D face towards a symmetric configuration, because it provides accurate point pairing with respect to the plane of symmetry. It can also reduce the computational complexity by dividing the given face into partitioned slices and facial feature regions. In addition, we use point-based representation over all stages of symmetrization without generating a consistent triangle mesh or texture parameterization, which makes it much easier to support discrete processes such as symmetric deformation and to be combined with more sophisticated manipulations later such as cutting of surfaces. Finally, we performed a statistical analysis to assess subjects' preference for the symmetrized faces by our approach. Copyright © 2009 John Wiley & Sons, Ltd. The pipeline of 3D face symmetrization. We present a novel method for enhancing the symmetry of a scanned 3D face automatically. We developed a new local 3D shape descriptor based on facial features and surface curvatures. Our shape descriptor can improve the accuracy when deforming a 3D face towards a symmetric configuration, because it provides accurate point pairing with respect to the plane of symmetry. In addition, we use point-based representation over all stages of symmetrization without generating a consistent triangle mesh or texture parameterization.