Surface shape and curvature scales
Image and Vision Computing
Two- and three-dimensional patterns of the face
Two- and three-dimensional patterns of the face
Three-Dimensional Model Based Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Detection of Anchor Points for 3D Face Veri.cation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Matching 2.5D Face Scans to 3D Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
3D+2D Face Localization Using Boosting in Multi-Modal Feature Space
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
3D face detection using curvature analysis
Pattern Recognition
Three-Dimensional Surface Mesh Segmentation Using Curvedness-Based Region Growing Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature detection using curvature maps and the min-cut/max-flow algorithm
GMP'06 Proceedings of the 4th international conference on Geometric Modeling and Processing
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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 k1, k2, 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.