Biometric Based Secure Machine Authentication Method Using Hybrid Learning

Journal of Industrial Engineering Research, Vol. 1(6), Pages: 1-6, September 2015

6 Pages Posted: 13 Jul 2017

See all articles by Ijaz Shoukat

Ijaz Shoukat

King Saud University

Abdullah Al-Dhelaan

King Saud University

Mznah Al-Rodhaan

King Saud University

Date Written: June 16, 2015

Abstract

Systematic authentication and usage of several other computerized machines is a great problem for patients having motor rehabilitation due to stroke or spinal injuries. Brain Computer Interface (BCI) technology provides an alternative heuristic of controlling the machines with just neural activity without imposing any physical effort. The recording of neural activity (brain biometric signals) associates several learning issues such as outlier, non-stationary patterns, multi-dimensions, variable training sizes, timing variations, noise and artifacts. Due to these issues, learning and classification provide inadequate results under any single classifier. Hybrid training and classification methods can overcome these issues but the question of performance and feasibility matters is still open in this case. Several hybrid classifiers have been analytically reviewed and evaluated in this article to propose optimal hybrid learning and classification method for BCI based medical or security systems. The analysis shows that mutual assembly of HMM-SVM-LDA under CSP is an optimum approach to escape majority of learning issues. The presented analysis is not only significant for the development of BCI based security systems but it is also important for medical applications

Keywords: Biometric, Electroencephalography, Brain-Computer-Interface, Learning, Classification

Suggested Citation

Shoukat, Ijaz and Al-Dhelaan, Abdullah and Al-Rodhaan, Mznah, Biometric Based Secure Machine Authentication Method Using Hybrid Learning (June 16, 2015). Journal of Industrial Engineering Research, Vol. 1(6), Pages: 1-6, September 2015, Available at SSRN: https://ssrn.com/abstract=2796703

Ijaz Shoukat (Contact Author)

King Saud University ( email )

P.O. Box 2460
Riyadh, 11451
Saudi Arabia

Abdullah Al-Dhelaan

King Saud University

P.O. Box 2460
Riyadh, 11451
Saudi Arabia

Mznah Al-Rodhaan

King Saud University

P.O. Box 2460
Riyadh, 11451
Saudi Arabia

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