Abstract
In this paper, a computational model has been developed to identify a face of an unknown person’s by utilizing eigenfaces as unique features and backpropagation Neural Network for recognition. The features of a basic human face are extracted using eigenfaces. These features are then used to identify an unknown face by using multiple numbers of backpropagation neural networks. Samples of 15 human faces are obtained from The ORL database. The experiments are compared to the effects of changes size of face images, different face images combination and different neural network parameter. The classification more than 90% for trained classes and 18% for untrained classes were achieved.
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© 2007 Springer-Verlag Berlin Heidelberg
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Hashim, M.F., Rizon, M., Saad, P., Osman, N.A.A. (2007). Multiple neural networks for Human Face Recognition. In: Ibrahim, F., Osman, N.A.A., Usman, J., Kadri, N.A. (eds) 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. IFMBE Proceedings, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68017-8_20
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DOI: https://doi.org/10.1007/978-3-540-68017-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68016-1
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