期刊名称：Journal of Computer Sciences and Applications
印刷版ISSN：2328-7268
电子版ISSN：2328-725X
出版年度：2015
卷号：3
期号：3
页码：67-72
DOI：10.12691/jcsa-3-3-2
语种：English
出版社：Science and Education Publishing
摘要：In this paper, we present an automatic 3D face recognition system based on the computation of the geodesic distance between the reference point and the other points in the 3D face surface. To compute a geodesic distance, we use the Fast Marching algorithm for solving the Eikonal equation. For space reduction, we apply Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (LDA). Quantitative measures of similarity are obtained and then used as inputs to several classification methods. In the classifying step, we use: Neural Networks (NN), k-Nearest Neighbor (KNN) and Support Vector Machines (SVM). To test this method and evaluate its performance, a simulation series of experiments were performed on 3D Shape REtrieval Contest 2008 database (SHREC2008).
关键词：3D face recognition; geodesic distance; reference point; Principal Components Analysis; Linear Discriminant Analysis; fast marching; eikonal equation
