Paper Info
Title
Geometry-based 3D face morphology analysis: soft-tissue landmark formalization
Abstract
The face is perhaps the most important human anatomical part, and its study is very important in many fields, such as the medical one and the identification one. Technical literature presents many works on this topic involving bi-dimensional solutions. Even if these solutions are able to provide interesting results, they are strongly subjected to images distortion. Thanks to the significant improvements obtained in the 3D scanner domain (photogrammetry for instance), today it is possible to replace the 2D images with more precise and complete 3D models (triangulated points clouds). Working on three-dimensional data, in fact, it is possible to obtain a more complete set of information about the face morphology. At present, even if it is possible to find interesting papers on this field, there is the lack of a complete protocol for converting the big amount of data coming from the three-dimensional point clouds in a reliable set of facial data, which could be employed for recognition and medical tasks. Starting from some anatomical human face concepts, it has been possible to understand that some soft-tissue landmarks could be the right data set for supporting many processes working on three-dimensional models. So, working in the Differential Geometry domain, through the Coefficients of the Fundamental Forms, the Principal Curvatures, Mean and Gaussian Curvatures and also with the derivatives and the Shape and Curvedness Indexes, the study has proposed a structured methodology for soft-tissue landmark formalization in order to provide a methodology for their automatic identification. The proposed methodology and its sensitivity have been tested with the involvement of a series of subjects acquired in different scenarios.
Year
DOI
Venue
2014
10.1007/s11042-012-1091-3
Multimedia Tools Appl.
Keywords
Field
DocType
3D models,3D scanners,Face morphology,Soft tissue landmarks
Computer vision,Photogrammetry,Pattern recognition,Computer science,Principal curvature,Gaussian,Triangulation,Differential geometry,Artificial intelligence,Landmark,Point cloud,Distortion
Journal
Volume
Issue
ISSN
68
3
1380-7501
Citations 
PageRank 
References 
10
0.56
13
Authors
2
Name
Order
Citations
PageRank
Enrico Vezzetti115112.98
Federica Marcolin2947.50