Abstract
The behavior of six curvature-based 3D shape descriptors which were computed on the surface of 3D face models, is studied. The set of descriptors includes k 1, k 2, Mean and Gaussian curvatures, Shape Index, and Curvedness. Instead of defining clusters of vertices based on the value of a given primitive surface feature, a face template composed by 28 anatomical regions, is used to segment the models and to extract the location of different landmarks and fiducial points. Vertices are grouped by: vertices themselves, region, and region boundaries. The aim of this study is to analyze the discriminant capacity of each descriptor to characterize regions and to identify key points on the facial surface. The experiment includes testing with data from synthetic face models and 3D face range images. In the results: the values, distributions, and relevance indexes of each set of vertices, were analyzed.
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Salazar, A., Cerón, A., Prieto, F. (2010). 3D Curvature-Based Shape Descriptors for Face Segmentation: An Anatomical-Based Analysis. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_36
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DOI: https://doi.org/10.1007/978-3-642-17277-9_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17276-2
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