Abstract
We have proposed the integration of behavior biometrics using Supervised Pareto learning SOM to improve the accuracy of authentication. For small systems such as mobile devices, this method may be heavy, because of the memory usage or computational power. In this paper, we propose the application of Concurrent Pareto learning SOM, which uses a small map for each user. The performance of this method is confirmed by authentication experiments using behavior biometrics of keystroke timings and key typing sounds.
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Dozono, H., Ito, S., Nakakuni, M. (2011). The Authentication System for Multi-modal Behavior Biometrics Using Concurrent Pareto Learning SOM. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_26
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DOI: https://doi.org/10.1007/978-3-642-21738-8_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21737-1
Online ISBN: 978-3-642-21738-8
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