Access Windows by Iris Recognition

This project aims to design and develop an iris recognition system for accessing Microsoft Windows. The system is built using digital camera and Pentium 4 with SVGA display adapter. MATLAB ver. 7.0 is used to preprocess the taken images convert the images into code and compare the picture code with...

Full description

Saved in:

Bibliographic Details
Main Author:	Ali, Musab A. M
Format:	Thesis
Language:	eng eng
Published:	2009
Subjects:	TK Electrical engineering Electronics Nuclear engineering QA76.76 Fuzzy System.
Online Access:	https://etd.uum.edu.my/1604/1/Musab_A.M._Ali.pdf https://etd.uum.edu.my/1604/2/1.Musab_A.M._Ali.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uum-etd.1604
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
topic	TK Electrical engineering Electronics Nuclear engineering QA76.76 Fuzzy System.
spellingShingle	TK Electrical engineering Electronics Nuclear engineering QA76.76 Fuzzy System. Ali, Musab A. M Access Windows by Iris Recognition
description	This project aims to design and develop an iris recognition system for accessing Microsoft Windows. The system is built using digital camera and Pentium 4 with SVGA display adapter. MATLAB ver. 7.0 is used to preprocess the taken images convert the images into code and compare the picture code with the stored database. The project involves two main steps: (1) applying image processing techniques on the picture of an eye for data acquisition. (2)applying Neural Networks techniques for identification .The image processing techniques display the steps for getting a very clear iris image necessary for extracting data from the acquisition of eye image in standard lighting and focusing. In a use of your images, the images are enhanced and segmented into 100 parts. The standard deviation is computed for every part in which the values are used for identification using NN techniques. Locating the iris is done by following the darkness density of the pupil. For all networks, the weights and output values are stored in a text file to be used later in identification. The Backprobagation network succeeded in identification and getting best results because it attained to (False Acceptance Rate = 10% - False Rejection Rate = 10%), while the Linear Associative Memory network attained to (False Acceptance Rate = 20% - False Rejection Rate = 20%)
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Ali, Musab A. M
author_facet	Ali, Musab A. M
author_sort	Ali, Musab A. M
title	Access Windows by Iris Recognition
title_short	Access Windows by Iris Recognition
title_full	Access Windows by Iris Recognition
title_fullStr	Access Windows by Iris Recognition
title_full_unstemmed	Access Windows by Iris Recognition
title_sort	access windows by iris recognition
granting_institution	Universiti Utara Malaysia
granting_department	College of Arts and Sciences (CAS)
publishDate	2009
url	https://etd.uum.edu.my/1604/1/Musab_A.M._Ali.pdf https://etd.uum.edu.my/1604/2/1.Musab_A.M._Ali.pdf
_version_	1747827174072647680
spelling	my-uum-etd.16042022-04-21T03:28:51Z Access Windows by Iris Recognition 2009 Ali, Musab A. M College of Arts and Sciences (CAS) College of Arts and Sciences TK Electrical engineering. Electronics Nuclear engineering QA76.76 Fuzzy System. This project aims to design and develop an iris recognition system for accessing Microsoft Windows. The system is built using digital camera and Pentium 4 with SVGA display adapter. MATLAB ver. 7.0 is used to preprocess the taken images convert the images into code and compare the picture code with the stored database. The project involves two main steps: (1) applying image processing techniques on the picture of an eye for data acquisition. (2)applying Neural Networks techniques for identification .The image processing techniques display the steps for getting a very clear iris image necessary for extracting data from the acquisition of eye image in standard lighting and focusing. In a use of your images, the images are enhanced and segmented into 100 parts. The standard deviation is computed for every part in which the values are used for identification using NN techniques. Locating the iris is done by following the darkness density of the pupil. For all networks, the weights and output values are stored in a text file to be used later in identification. The Backprobagation network succeeded in identification and getting best results because it attained to (False Acceptance Rate = 10% - False Rejection Rate = 10%), while the Linear Associative Memory network attained to (False Acceptance Rate = 20% - False Rejection Rate = 20%) 2009 Thesis https://etd.uum.edu.my/1604/ https://etd.uum.edu.my/1604/1/Musab_A.M._Ali.pdf text eng public https://etd.uum.edu.my/1604/2/1.Musab_A.M._Ali.pdf text eng public masters masters Universiti Utara Malaysia Amer, R. (2001). "Design a software application for Iris Identification by Artificial Neural Network", M. Sc.Thesis, Computers and Mathematical Sciences College, University of Mosul/ Iraq.Al-Sumaidaee, H. A. (2005). "Off-Line Handwritten Signature Verification Using Backpropagation Neural Network", M. Sc. Thesis, Technical College, Mosul/Iraq.Aminzadeh, F. Barhen J. Glover, C. W. and Toomarian, N. B. (2000). Reservoir parameter estimation using hybrid neural network, Computers & Geoscience,vol. 26, pp. 869-875.Berry, M. J. A. and Linoff, G.(1997). Data Mining Techniques, John Wiley & Sons,Inc.Burghardt,T.(2000) "Inside Iris Recognition", Master course in global computing and multimedia, university of Bristol, November. Cappelli, D. Maio, D. Maltoni, J. L. Wayman, and A. K. Jain, (2006). Performance evaluation of fingerprint verification systems, IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 1, pp. 3–18.Chang, W. Y (2004). "Image Processing with Wreath Products", M. Sc. Thesis, Harvey Mudd college, Department of Mathematics.Chang, H. C. Kopaska-Merkel, D. C. Chen, H. C. and Durrans, R. Lithofacies (2000).Identification Using Multiple Adaptive Resonance Theory of Neural Networks and Group Decision Expert System, Computers and Geosciences vol 26, pp.591- 601.Chen H. C. and Fang. J. H. (1993). A new method for prospect appraisal. AAPG Bull., vol. 77, pp. 9-8.Dory L. Hudspeth, Sarah J. Spinks, (2001). "Iris Scan Technology", Faulkner Information Services, and Publication Date: 0109, Publication Type:TUTORIAL.Daugman, J. (1993). "High Confidence Visual Recognition of Persons by a Test of Statistical Independence", IEEE transactions on pattern analysis and machine intelligence. Vol. 15, No. 11. Daugman, J. (2004). "How Iris Recognition Works", IEEE transactions on circuits and systems for video technology, Vol. 14, No.1.Daugman, J. Downing, C. (2001). "Epigenetic randomness, complexity and singularity of human iris patterns", The Royal Society, Proc. R. Soc. Lond. B 268, 1737-1740.Daugman, J. (2003). "The importance of being random: statistical principles of iris recognition", The journal of the pattern recognition society, Pattern Recognition 36, 279 – 291.Dunker, M. (2004). "Don't blink: Iris recognition for biometric identification", SANS institute as part of GIAC practical repository, pp.1-17. Daugman, J. G. (1993). “High Confidence Visual Recognition of Persons by a Test of Statistical Independence”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.15, no.11, pp.1148-1161.Daugman, J. (1994). Biometric personal identification system based on iris analysis.U.S. Patent No. 5,291,560.Daugman, J. (2005). Results from 200 billion iris cross-comparisons. Technical Report UCAM-CL-TR-635, University of Cambridge Computer Laboratory,June 2005. Fausett, L. (1994). "Fundamental of Neural Networks, Architectures, Algorithms and Applications", Printice Hall Int.Hocquet, S. Ramel, J. Cardot, H. (2005). Fusion of Methods for Keystroke Dynamic Authentication, Automatic Identification Advanced Technologies, 2005. Fourth IEEE Workshop Page(s):224 - 229 Istook, E. Martinez, T. (2002). "Improved back propagation learning in neural networks with windowed momentum", International journal of neural systems,vol. 12, no. 3&4, pp. 303-318. Jain, A. K. (2004). "Biometric recognition: how do I know who you are?" Signal Processing and Communications Applications Conference. Proceedings of the IEEE 12th: 3 - 5 Jain, A. K. Ross, A. & Pankanti, S., (2006). "Biometrics: A Tool for Information Security", IEEE Transactions on Information Forensics and Security.Johnson, R. G. (1991). “Can iris patterns be used to identify people?” Chemical and Laser Sciences Division LA-12331-PR, Los Alamos National Laboratory, Los Alamos, Calif.Kukula, E. Elliott, S. (2005). Implementation of Hand Geometry at Purdue University's Recreational Center: An Analysis of User Perspectives and System Performance, IEEE.Lim, J. S. (1990). "Two-dimensional signal and image processing", Prentice Hall signal processing series.Lawton, G. (1998). “Biometrics: A New Era in Security”, IEEE Computer, pp.16-18.Mohammed, R, A. Ibrahim, N, B. Nori, A, S. (2004) "Iris Identification Software(IIS)", Raf. Jour. Sci., Vol. 15, No. 1Comp. Math. & Stati. Special Issue, pp. 1-10, Maerivoet, S. (2000). "An introduction to image enhancement in the spatial domain",University of Antwerp, Department of mathematics and computer science.Omlin, C. W. and Giles, C. L. (2008). Symbolic Knowledge Representation in Recurrent Neural Networks: Insights from Theoretical Models of Computation.Golden, R. M, (1996). Mathematical Methods for Neural Network Analysis and Design. Cambrigde, MA: MIT Press.Patina, F (2003). "An introduction to digital image processing", homepage yov408, pp 1-49.Philips, P. J. Grother, P. R. Micheals, R. Blackburn, D. M. Tabassi, and J. M.(2002). Bone, Face Recognition Vendor Test. Zhu, Y. Tan, T. Wang, Y (2000). "Biometric Personal Identification Based on Iris Patterns", Proc. Int. Conf. on Pattern Recognition, Vol. 2, pp. 801-804.Roizenblatt, R. Schor, P. Dante, F. Roizenblatt, J. Belfort, R. (2004). "Iris recognition as a biometric method after cataract surgery", BioMedical Engineering 3:2, pp. 1-7.Riha, Z. Matyas, V. (2000). "Biometric Authenrication Systems", Faculty of Informatics Masaryk University (FIMU), FIMU-RS. Rosenzweig, P. Kochems, A. Schwartz, A. (2004). "Biometric Technologies: Security, Legal, and Policy Implications", Published by The Heritage Foundation, Legal Memorandum. Russ, J. C. (2004). "Seeing the Scientific Image", Proceedings RMS, Vol. 39/2.Shindala, N. (2004). "Speaker Recognition using neuro-fuzzy method", M. Sc. Thesis, Computer Engineering college, university of Mosul/Iraq, 2004.Umbaugh, S. E (1998). "Computer vision and image processing: A practical approach using CVIPtools", Prentice Hall PTR.Fisher, R. Perkins, S. Walker A. and Wolfart, E. (2003). "Laplacian / Laplacian of Gaussian".Wildes, R. P. (1997). “Iris Recognition: An Emerging Biometric Technology”,Proceedings of the IEEE, vol.85, pp.1348-1363. Wilson, A. R. Hicklin, H. Korves, B. Ulery, M. Zoepfl, M. Bone, P. Grother, R. J.Micheals, S. Otto, and C. Watson, (2004). Fingerprint vendor technology evaluation 2003: summary of results and analysis report, NIST Internal Rep. 7123.Wong, P. M. Gedeon, T. D. and Taggart, I. J. (1995). An improved technique in prediction: A neural network approach, IEEE Trans. Geosci. Remote Sensing,vol. 33, pp. 971.Zhang, D. (2000) “Biometrics Technologies and Applications”, Proc. of International Conference on Image and Graphics, pp.42-49, Tianjing, China.Zurada, J. M. (2004) Knowledge-Based Neurocomputing, The MIT Press, Massachusetts. p.65,.Fornell, Claes and Jaesung Cha (2004), “Partial Least Squares,” in Advanced Methods of Marketing Research, Richard P. Bagozzi, ed. Cambridge, MA:Blackwell, 52-78.Garbarino, Ellen and Mark S. Johnson (1999), "The Different Roles of Satisfaction,Trust, and Commitment in Customer Relationships," Journal of Marketing, 63 (2), 70-87.Grossman, Randi P. (2008), "Developing and Managing Effective Consumer Relationships," Journal of Product and Brand Management, 7 (1), 27-40.Gustafsson, Anders and Michael D. Johnson (2004), “Determining Attribute Importance in a Service Satisfaction Model”, Journal of Service Research,Volume 7, No. 2, November 2004 124-141. Hand, D., H. Mannila, and P. Smyth, (2001), Principles of Data Mining. MIT Press,2001.Hastie, T., R. Tibshirani and J. Friedman (2001), The Elements of Statistical Learning Data Mining, Inference, and Prediction. Springer. Hruschka, Harald (2001), An Artificial Neural Net Attraction Model (Annam) To Analyze Market Share Effects Of Marketing Instruments, Schmalenbach Business Review u Vol. 53 u January 2001 u pp. 27 – 40 Hahn, Carsten, Michael D. Johnson, Andreas Herrmann and Frank Huber (2002),“Capturing Customer Heterogeneity Using A Finite Mixture PLS Approach”, Schmalenbach Business Review, Vol. 54, July 2002, 243 – 269 Johnson, Michael and Anders Gustafsson (2000), Improving Customer Satisfaction,Loyalty and Profit: An Integrated Measurement and Management System. San Francisco: Jossey-Bass.Jones, Tim And Shirley, F. Tayler (2003). The Conceptual Domain of Service Loyalty: How Many Dimensions? Unpublished manuscript.Keiningham, Timothy L. ,Tiffany Perkins-Munn and Heather Evans (2003), “The Impact of Customer Satisfaction on Share Of Wallet in a Business-to-Business Environment”, Journal of Service Research, Vol6, No. 1, August, 2003, 37-50 Kumar, Piyush (1998), “A Reference-Dependent Model of Business Customers’Repurchase Intent,” working paper,William Marsh Rice University, Houston, TX.Mittal, Vikas and Patrick M. Baldasare (1996), “Impact Analysis and the Asymmetric Influence of Attribute Performance on Patient Satisfaction,” Journal of Health Care Marketing, 16 (3), 24-31.Mittal, Vikas and Jerome Katrichis (2000), “Distinctions between New and Loyal Customers,” Marketing Research, 12 (Spring), 27-32. Mittal, Vikas, William T. Ross, and Patrick M. Baldasare (1998), “The Asymmetric Impact of Negative and Positive Attribute-Level Performance on Overall Satisfaction and Repurchase Intentions,” Journal of Marketing, 62 (January), 33-47.Oliver, Richard L. (1997), Satisfaction: A Behavioral Perspective on the Consumer.New York: McGraw-Hill.Oliver, Richard L (1999), "Whence Consumer Loyalty," Journal of Marketing, 63(Special Issue), 33-44.Pritchard, Mark P., Mark E. Havitz, and Dennis R. Howard (1999), "Analyzing the commitment-loyalty link in service contexts," Journal of the Academy of Marketing Science, 27 (3), 333-48. Pugesek, B. H., A. Tomer, A. and A. Von Eye (2003), Structural Equation Modeling:Applications in Ecological and Evolutionary Biology. Cambridge University Press.Sharma, Neeru and Paul G. Patterson (2000), "Switching costs, alternative attractiveness, and experience as moderators of relationship commitment in professional, consumer services.," International Journal of Service Industry Management, 11 (5), 470-90.Reichheld, Frederick (1996). The Loyalty Effect: The Hidden Source Behind Growth,Profits, and Lasting Value. Boston: Harvard Business School Press.

Access Windows by Iris Recognition

Similar Items