Home   >   CSC-OpenAccess Library   >    Manuscript Information
Biometric Authentication Based on Hash Iris Features
Dalal N. Hammod
Pages - 1 - 11     |    Revised - 31-05-2020     |    Published - 30-06-2020
Volume - 13   Issue - 1    |    Publication Date - June 2020  Table of Contents
Biometric, Iris Features, Laplace Mask, Authentication System, Hash Function.
With an increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention since its introduction in 1992. In this study, authentication system contained two parts: registration part and matching part. In both parts, iris image is used for personal identification. Localization of inner boundary only, extracted a region from the iris (without eyelashes problem), a feature vector is deduced from the texture of the image. The feature vector is used for classification of the iris texture, then it's treated by the hash function to produce the hash value (authentic value of a person). In matching part, produced hash value searched in the authorized person's database for taking a decision (success or fail) of the authentication. The method was evaluated on iris images takes from the CASIA iris image database version 1.0 [15]. The experimental results show that the vector extracted by the proposed method has very discriminating values that led to a recognition rate of over 100% on iris database. Also, authentication system is very accurate because it's used a secure method of authentication that iris-biometric and a hash function for avoiding stealing data from database.
1 Google Scholar 
2 refSeek 
3 Doc Player 
4 Scribd 
5 SlideShare 
"CASIA iris image Database". http://www.sinobiometrics.com/Databases.htm, (2007).
A. Stoianov. "Biometric Encryption: A Positive-Sum Technology that Achieves Strong Authentication, Security AND Privacy". Information and Privacy Commissioner of Ontario-Toronto, Ontario CANADA 2007.
C. Tisse, L. Martain, etal. " Person Identification Technique Using Human Iris Recognition". France University de Montpellier, Proceedings of the 15th International Conference on Vision Interface, PP. 294-299, 2000.
C.M. Patil and S. Patilkulkarani. "Iris Feature Extraction for Personal Identification using Lifting Wavelet Transform". International Journal of computer Applications(0975-8887),Vol. 1,No. 14, 2010.
C.T. CHU and C. CHEN. "High Performance Iris Recognition Based on 1-D Circular Feature Extraction". I-Shou University (Taiwan), 2005.
F. MSHTML. "Advantages and disadvantages of the Iris for Identification". Technologies Journal, Vol. 7, No. 34, 2019.
H. Zuher." Hand Geometry-based on Identity Authentication Method". M.Sc. thesis, College of science, Al-Nahrain University, 2003
L. Masek."Recognition of Human Iris Patterns for Biometric Identification". Bachelor's Dissertation, School of Computer Science and Software Engineering, the University of Western Australia, 2003.
L. Montecchi1, P. Lollini, A. Bondavalli, E. L. Mattina." Quantitative Security Evaluation of a Multi-Biometric Authentication System ". Springer-Verlag Berlin Heidelberg 2011.
P. Robichaux. "Motivation Behind Iris Detection". Connexions Project, 2004. cnx.org/content/m12488/latest/-12k.
R. C. Gonzalez and R. E.Woods. "Digital Image Processing". printice-Hell, 2002.
R. N. Brink and R. I. Scollan. "Usability of Biometric Authentication Methods for Citizens with Disabilities". The MITRE Corporation-MIT RE T E C HN I C A L R E P OR T, 2019.
R.Y.Fatt Ng, Y.H. Tay and K.M.Mok. "An Effective Segmentation Method for Iris Recognition System". Malaysia, 2007.
S. E. Umbaugh. "Computer Vision and Image Processing". Printic-Hell,1998.
S. Trewin1, C. Swart1, L. Koved1, J. Martino1, K. Singh1, S. Ben-David. "Biometric Authentication on a Mobile Device: A Study of User Effort, Error and Task Disruption ". ACSAC '12 Dec. 3-7, Orlando, Florida USA, 2012.
Dr. Dalal N. Hammod
University of Al-Nahrain, College of Science, Computer Science Department, Baghdad - Iraq