Implementation of Daugman’s Algorithm and Adaptive Noise Filtering Technique for Digital Recognition of Identical Twin using MATLAB

International Journal of Computer Science and Engineering

© 2018 by SSRG - IJCSE Journal

Volume 5 Issue 9

Year of Publication : 2018

Authors : Oleka Chioma Violet, Ugwu Chukwuka Kennedy

10.14445/23488387/IJCSE-V5I9P103

How to Cite?

Oleka Chioma Violet, Ugwu Chukwuka Kennedy, "Implementation of Daugman’s Algorithm and Adaptive Noise Filtering Technique for Digital Recognition of Identical Twin using MATLAB," SSRG International Journal of Computer Science and Engineering , vol. 5,  no. 9, pp. 1-5, 2018. Crossref, https://doi.org/10.14445/23488387/IJCSE-V5I9P103

Abstract:

This paper presents the implementation of Daugman algorithm and adaptive noise filtering technique for digital recognition of identical twin. The main aim of this work is to present a novel epistemology that will differentiate identical twin using the integro- differential operator model of John Daugman. However, the challenge of this algorithm is (background impurity) white noise from the eye sclera, this work employs the best filtering technique called adaptive noise filtering process together with other image processing techniques for this work. Also the research paper presents another global application of Daugman algorithm for identical twin recognition which has not been solved till date even by face recognition systems. This work will highly improve investigation, eliminate impersonation, and stop mistake identity arrest of suspect to mention a few among other benefits. The work will be demonstrated using the matlab development tool.

Keywords:

face recognition, identical twin, investigation, impersonation

References:

1] Robert H Zakon .Hobbes' Internet Timeline. Significant dates in the history of the Internet. 2010 
[2] Oad Percy and Ahmad Waqas, Iris localization using Daugman’s algorithm, 2010 
[3] H.Rowley, S. Baluja, and T. Kanade.Neural network-based face detection. In Proc. IEEE Conf.on Computer Visioin and Pattern Recognition, pages 203-207, San Francisco, CA, 1996 
[4] Kah-Kay Sung and TomasoPoggio. Example-based learning for view-based human face detection.A.I. Memo 1521, CBCL Paper 112, MIT, December 1994 
[5] B.Moghaddam and A. Pentland.Probabilistic visual learning for object representation.In S.K.Nayar and T. Poggio, editors, Early Visual Learning, pages 99--130. Oxford Univ. Press,1996. 
[6] O.Jesorsky, K. J. Kirchberg, R.W. Frischholz. Robust Face Detection Using the HausdorffDistance.In Proc. Third International Conference on Audio- and Video-based BiometricPerson Authentication, Halmstad, Sweden, 2001 
[7] Vimal, VirenderKadyan, Implementation and Performance Analysis of Face Recognition Using MATLAB. Dept. of Computer Science and Engineering, RNCET, Panipat, Haryana,.IJCST Vol. 6, Issue 2, April - June 2015 ISSN : 0976-8491 (Online) | ISSN : 2229-4333. 
[8] NawafHazimBarnouti. Face Recognition Using Eigen-Face Implemented On DSP Processor. International Journal of Engineering Research and General Science Volume 4, Issue 2, March-April, 2016, ISSN 2091-2730 
[9] Wikipedia project 2017. 
[10] Daugman J. "How iris recognition works." IEEE Trans. CSVT, vol. 14, no. 1. 2004. 
[11] Mark S. Nixon and Alberto S. Aguado. Feature extraction and image processing. AcademicPress, 2008 
[12] Lim, Jae S., Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ, Prentice Hall, 1990, p. 548, equations 9.26, 9.27, and 9.29 
[13] Mathworks documentation 2017a.