Abstract
The depth information in the face represents personal features in detail. In this study, the important personal facial information was presented by the surface curvatures and the features of vertical and horizontal of nose volume extracted from the face. The approach works by the depth of nose, the area of nose and the volume of nose based both on a vertical and horizontal are calculated. And the principal components analysis (PCA), which is calculated using the curvature data, was presented different features for each person. To classify the faces, the cascade architectures of fuzzy neural networks (CAFNNs), which can guarantee a high recognition rate as well as parsimonious knowledge base, are considered. In the experimental results, 3D images demonstrate the effectiveness of the proposed methods.
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© 2006 Springer-Verlag Berlin Heidelberg
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Lee, Y., Kim, I., Shim, J., Marshall, D. (2006). 3D Facial Image Recognition Using a Nose Volume and Curvature Based Eigenface. In: Kim, MS., Shimada, K. (eds) Geometric Modeling and Processing - GMP 2006. GMP 2006. Lecture Notes in Computer Science, vol 4077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11802914_48
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DOI: https://doi.org/10.1007/11802914_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36711-6
Online ISBN: 978-3-540-36865-6
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