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Proceedings Paper

Genetic algorithms in optimization of 3D face recognition system using cylindrical-hidden layer neural network in its eigenspace domain
Author(s): Benyamin Kusumoputro; Martha Yuliana Pangabean; Leila Fatmasari Rachman
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Paper Abstract

In this paper, a 3D face recognition system is developed using a cylindrical structure of hidden layer neural network and its optimization through genetic algorithms. The cylindrical structure of hidden layer is constructed by substituting each of neuron in its hidden layer of conventional multilayer perceptron with a circular structure of neurons. The neural system is then applied to recognize a real 3D face image from a database that consists of 5 Indonesian persons. The images are taken under four different expressions such as neutral, smile, laugh and free expression. The 2D images are taken from the human model by gradually changing visual points, which is done by successively varying the camera position from -90 to +90 with an interval of 15 degrees. The experimental result has shown that the average recognition rate of about 64% could be achieved when we used the image in its spatial domain and about 84% when the image data is transformed to its eigen domain. Optimization of the hidden neurons is accomplished using genetic algorithms, which reduced the active neurons up to about 63.7% while increasing the recognition rate into about 94% in average.

Paper Details

Date Published: 11 February 2002
PDF: 10 pages
Proc. SPIE 4567, Machine Vision and Three-Dimensional Imaging Systems for Inspection and Metrology II, (11 February 2002); doi: 10.1117/12.455244
Show Author Affiliations
Benyamin Kusumoputro, Univ. of Indonesia (Indonesia)
Martha Yuliana Pangabean, Univ. of Indonesia (Indonesia)
Leila Fatmasari Rachman, Univ. of Indonesia (Indonesia)


Published in SPIE Proceedings Vol. 4567:
Machine Vision and Three-Dimensional Imaging Systems for Inspection and Metrology II
Kevin G. Harding; John W. V. Miller, Editor(s)

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