Abstract
This Paper studies different committees of neural networks for biometric pattern recognition. We use the neural nets as classifiers for identification and verification purposes. We show that a committee of nets can improve the recognition rates when compared with a multi-start initialization algorithm that just picks up the neural net which offers the best performance. On the other hand, we found that there is no strong correlation between identification and verification applications using the same classifier.
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Faundez-Zanuy, M. (2005). Study of a Committee of Neural Networks for Biometric Hand-Geometry Recognition. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_145
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DOI: https://doi.org/10.1007/11494669_145
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
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