Biometric Authentication of Individual using M-Wave Signals
Samer Chantaf1, Amine Naït-Ali2, Mahmoud Abbas3, Mohamad Khalil4
1Dr. Samer Chantaf, Department of Computer Communication and Networks Engineering, Lebanese University, University Institute of Technology, Saida, Lebanon.
2Amine Nait-Ali, Laboratoire Images Signaux et Systèmes Intelligents (LISSI, EA 3956), Université Paris-Est Créteil (UPEC), Créteil, France.
3Dr. Mahmoud Abbas, Department of Computer Communication and Networks Engineering, Lebanese University, University Institute of Technology, Saida, Lebanon.
4Mohamad Khalil, Lebanese University, Doctoral School for Sciences and Technology, Tripoli, Lebanon.
Manuscript received on 23 July 2015 | Revised Manuscript received on 30 July 2015 | Manuscript published on 30 July 2015 | PP: 53-57 | Volume-4 Issue-3, July 2015 | Retrieval Number: C1468074315©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper suggests a new biometric method of human verification based on muscle response following an electrical stimulation is presented in this study. The corresponding response is called M-wave. The goal is to study the possibility of using the M-wave signals to verify an individual. In this work, parameters are extracted by modeling the M-waves using wavelet networks. The radial basis neural network method is then used to classify these parameters. This method has been evaluated on a set of M-wave responses corresponding to normal individuals. Consequently, very encouraging results have been obtained.
Keyword: M-wave; biometrics; wavelet networks; neural network; classification.
Scope of the Article: Classification.