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November Edition 2017

International Journal of Applied Information Systems solicits high quality original research papers for the November 2017 Edition of the journal. The last date of research paper submission is October 16, 2017.

Trimodal Biometric Authentication System using Cascaded Link-based Feed forward Neural Network [CLBFFNN]

Benson-Emenike Mercy E., Sam-Ekeke Doris C. Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Benson-Emenike Mercy E., Sam-Ekeke Doris C.
10.5120/ijais2017451699
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  1. Benson-Emenike Mercy E. and Sam-Ekeke Doris C.. Trimodal Biometric Authentication System using Cascaded Link-based Feed forward Neural Network [CLBFFNN]. International Journal of Applied Information Systems 12(5):7-19, August 2017. URL, DOI BibTeX

    @article{10.5120/ijais2017451699,
    	author = "Benson-Emenike Mercy E. and Sam-Ekeke Doris C.",
    	title = "Trimodal Biometric Authentication System using Cascaded Link-based Feed forward Neural Network [CLBFFNN]",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "August 2017",
    	volume = 12,
    	number = 5,
    	month = "August",
    	year = 2017,
    	issn = "2249-0868",
    	pages = "7-19",
    	url = "http://www.ijais.org/archives/volume12/number5/996-2017451699",
    	doi = "10.5120/ijais2017451699",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

The beginning of the 21st century was rich in events that turned the world’s attention to public security. Increase in technological advancement gave people possibilities of information transfer and ease of physical mobility unseen before. With those possibilities comes risk of fraud, theft of personal data, or even theft of identity. One of the ways to prevent this is through biometric authentication system. This paper considers a multi-biometric system involving a combination of three biometric traits: iris, fingerprint and face in order to make authentication cheaper and more reliable. When the images are captured using optical scanner and webcam, image pre-processing is done using Enhanced Extracted Face (EEF), Plainarized Region of Interest (PROI) and Advanced Processed Iris Code (APIC) methods for face, fingerprint and iris images respectively. These are fed into a Cascaded Link-Based Feed Forward Neural Network (CLBFFNN) which is a classifier trained with back-propagation algorithm. CLBFFNN comprises of CLBFFNN(1) used for training and CLBFFNN(2) used as the main classifier. Fusion of outputs from face, fingerprint and iris recognition systems is done at decision level using AND operation. With the use of the improved pre-processing methods, Optical Character Recognition (OCR) with intelligent barcode and CLBFFNN, the proposed intelligent multibiometric system is proved to be cheaper, more secure and efficient than the existing methods.

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Keywords

Authentication, Enhanced Extracted Face (EEF), Plainarized Region Of Interest (PROI), Advanced Processed Iris Code (APIC), Cascaded Link-Based Feed Forward Neural Network (CLBFFNN), Optical Character Recognition (OCR), Multibiometric, Back propagation Algorithm, Adaptive Principal Component Analysis (APCA), Multilayer Perceptron (MLP), Relevant Vector Machine (RVM), and Support Vector Machine (SVM)