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T. Haar, F. B. and R.C. Veltkamp. SHREC’08 entry: 3D face recognition using facial contour curves. In SMI ’08: Proceedings of the IEEE International Conference on Shape Modeling and Applications, 259-260, Stony Brook, NY, USA, 2008.

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Article

Three Dimensional Face Surfaces Analysis using Geodesic Distance

1Interdisciplinary Laboratory of Research in Sciences and Technologies (LIRST), Sultan Moulay Slimane University, Beni Mellal, Morocco


Journal of Computer Sciences and Applications. 2015, Vol. 3 No. 3, 67-72
DOI: 10.12691/jcsa-3-3-2
Copyright © 2015 Science and Education Publishing

Cite this paper:
Rachid AHDID, Said SAFI, Bouzid MANAUT. Three Dimensional Face Surfaces Analysis using Geodesic Distance. Journal of Computer Sciences and Applications. 2015; 3(3):67-72. doi: 10.12691/jcsa-3-3-2.

Correspondence to: Rachid  AHDID, Interdisciplinary Laboratory of Research in Sciences and Technologies (LIRST), Sultan Moulay Slimane University, Beni Mellal, Morocco. Email: r.ahdid@usms.ma

Abstract

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).

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