Abstract
In this paper, a new efficient method is proposed based on the radial basis function neural networks (RBFNs) architecture for human face recognition system using a soft computing approach. The performance of the present method has been evaluated using the BioID Face Database and compared with traditional radial basis function neural networks. The new approach produces successful results and shows significant recognition error reduction and learning efficiency relative to existing technique.
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References
BioID face database: http://www.bioid.com/downloads/facedb/index.php
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Pensuwon, W., Adams, R., Davey, N., Taweepworadej, W. (2006). Improving Radial Basis Function Networks for Human Face Recognition Using a Soft Computing Approach. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_8
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DOI: https://doi.org/10.1007/11903697_8
Publisher Name: Springer, Berlin, Heidelberg
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