The Improved Biometric Identification of Keystroke Dynamics Based on Deep Learning Approaches
Author:
Affiliation:
1. Department of Applied Informatics, Faculty of Automatic Control, Electronics and Computer Sciences, Silesian University of Technology, 44-100 Gliwice, Poland
2. Healthcare Solutions Department, NubiSoft, 44-100 Gliwice, Poland
Abstract
Publisher
MDPI AG
Link
https://www.mdpi.com/1424-8220/24/12/3763/pdf
Reference28 articles.
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4. Biometric Authentication and Identification Using Keystroke Dynamics: A Survey;Banerjee;J. Pattern Recognit. Res.,2012
5. Messerman, A., Mustafi, T., Camtepe, S., and Albayrak, S. (2011, January 11–13). Continuous and non-intrusive identity verification in real-time environments based on free-text keystroke dynamics. Proceedings of the 2011 International Joint Conference on Biometrics, IJCB 2011, Washington, DC, USA.
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