research-article

An Improved Iris Recognition System Based on Possibilistic Modeling

Authors:

Jihen Frikha Elleuch,

Imen Khanfir KallelAuthors Info & Claims

MoMM 2015: Proceedings of the 13th International Conference on Advances in Mobile Computing and Multimedia

Pages 26 - 32

https://doi.org/10.1145/2837126.2837156

Published: 11 December 2015 Publication History

Abstract

The biometric systems face variability, incompleteness and insufficiency in data, which affects the performance of the recognition system. In iris recognition systems, several conditions cause different types of degradations on iris data such as the poor quality of the acquired pictures, the iris region which can be partially occluded due to light spots, or by lenses, eyeglasses, hair or eyelids, and adverse illuminations or contrasts. All of these limitations are open problems in the iris recognition and affect the performance of iris localization, iris feature extraction or decision making process, and appear as imperfections in the extracted signature. This paper addresses the use of the uncertainty theory for modeling iris system imperfections. Several comparative experiments were conducted on three subsets, namely CASIA.Ver4: synthetic, thousand and interval iris databases. Experimental results show that our proposed system, based on the possibility theory, improves the iris recognition system in terms ROC, AUC, FAR, FRR and PIN, compared to other iris identification systems.

References

[1]

Li, P., Liua, X., Xiaoa, L., and Songa, Q. 2010. Robust and accurate iris segmentation in very noisy iris images. Published in Elsevier journal of Image and Vision Computing, vol.28, no.2, 246--253.

Digital Library

[2]

Grother, P., Matey, J.R., Tabassi, E., Quinn, G.W., Chumakov, M. 2013. In Proceedings of the IREX VI program on Temporal Stability of Iris Recognition Accuracy (Iris Ageing). Information Access Division, National Institute of Standards and Technology (NIST, USA, July 24, 2013).

[3]

Moi, S.H., Asmuni, H., Hassan, R., Othman, R.M. 2014. Multimodal biometrics: Weighted score level fusion based on non-ideal iris and face images. Published in Elsevier journal of Expert Systems with Applications.

[4]

Smets, P. 1996. Imperfect information: Imprecision and uncertainty. Published in Kluwer Academic book of Uncertainty Management in Information Systems, pp. 225--254, Boston.

[5]

Zadeh, L.A. 1965. Fuzzy sets Information and Control. 8, 338--353.

[6]

Shafer, G.A. 1976. Mathematical Theory of Evidence. University of Princeton. Press, Princeton.

[7]

Dubois, D. and Prade, H. 1988. Possibility theory: An approach to computerized processing of uncertainty. Plenum Press, New York.

[8]

Zadeh, L.A. 1978. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and systems, pp. 3--28, USA.

[9]

Cooman, G. and Aeyels D. 2000. A random set description of a possibility measure and its natural extension. IEEE Transactions on Systems, Man and Cybernetics, 30, pp. 124--130.

Digital Library

[10]

Dubois, D., Cayrac, D. and Prade, H. 1996. Handling uncertainty with possibility theory and fuzzy sets in a satellite fault diagnosis application. Proceedings of the 6th International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems, pp. 251--269.

[11]

Benferhat, S., Dubois, D., Prade, H. and Williams, M.A. 1999."A practical approach to fusing and revising prioritized belief bases. Proceedings of the 9th Portuguese Conference on Artificial Intelligence, pp. 222--236.

Digital Library

[12]

He, L. P., Huang, H. Z. and Du, L. 2007. A review of possibilistic approaches to reliability analysis and optimization in engineering design, Lecture Notes in Computer Science, vol. 4553, Part IV, pp. 1075--1084.

Digital Library

[13]

Bodorin, N.P. and Balas, V.E. 2014. Fuzzy Membership, Possibility, Probability and Negation in Biometrics. Acta Polytechnica Hungarica, Vol. 11, No. 4.

[14]

Jenhani, I. 2010. From Possibilistic Similarity Measures to Possibilistic Decision Trees. Thesis for obtaining the PhD, University of Artois, French.

[15]

Dubois D., Prade, H. and Sandri, S. 1993. On possibility/probability transformations. Fuzzy Logic, pp. 103--112.

[16]

Masson, M. and Denoeux, T. 2006. Inferring a possibility distribution from empirical data. Fuzzy Sets and Systems, 157(3), pp. 319--340.

Digital Library

[17]

Dubois, D. and Prade, H. 2006. Représentations formelles de l'incertain et de l'imprécision. Book Concepts et méthodes pour l'aide à la décision, vol.1, pp. 111--165.

[18]

He, F., Liu, Y., Zhu, X., Huang, C., Han, Y. and Dong, H. 2014. Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters. EURASIP Journal on Advances in Signal Processing 2014.

[19]

CASIA Iris Database. Institute of Automation, Chinese Academy of Sciences, DOI= http://biometrics.idealtest.org/.

[20]

Nabti, M. and Bouridane, A. 2008. An effective and fast iris recognition system, Pattern Recognition (41), No. 3, pp. 868--879.

Digital Library

[21]

Daugman, J.G. 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, pp. 1148--1160.

Digital Library

[22]

Campos, L.M. and Huete, J.F. 2001. Measurement of possibility distributions. Int. J.General Systems, vol. 30(3), pp. 309--346.

[23]

Goodman, L.A. 1965. On simultaneous confidence intervals for multinomial proportions. Technometrics, Vol. 7 No. 2, pp. 247--254.

[24]

Sison, C.P and Glaz, J. 1995. Simultaneous confidence intervals. Journal American Statistical Association, vol. 90, No. 429, pp. 366--369.

[25]

May, W.L. and Johnson, W.D. 1997. A SAS macro for constructing simultaneous confidence intervals for multinomial proportions. Computer Methods and Programs in Biomedecine 53, pp. 153--162.

Cited By

Soni APatidar TM RP BS BRajendran R(2021)Iris Recognition using Hough Transform and Neural Architecture Search Network2021 Innovations in Power and Advanced Computing Technologies (i-PACT)10.1109/i-PACT52855.2021.9696917(1-5)Online publication date: 27-Nov-2021
https://doi.org/10.1109/i-PACT52855.2021.9696917
Mei W(2019)Probability/Possibility Systems for Modeling of Random/Fuzzy Information with Parallelization ConsiderationInternational Journal of Fuzzy Systems10.1007/s40815-019-00627-9Online publication date: 2-Apr-2019
https://doi.org/10.1007/s40815-019-00627-9
Bellaaj MBoukhris RDamak ASellami D(2016)Possibilistic modeling palmprint and fingerprint based multimodal biometric recognition system2016 International Image Processing, Applications and Systems (IPAS)10.1109/IPAS.2016.7880147(1-8)Online publication date: Nov-2016
https://doi.org/10.1109/IPAS.2016.7880147

An Improved Iris Recognition System Based on Possibilistic Modeling
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning

Recommendations

Improving the Performance of Iris Recogniton System Using Eyelids and Eyelashes Detection and Iris Image Enhancement
ICCI '06: Proceedings of the 2006 5th IEEE International Conference on Cognitive Informatics - Volume 02

Iris recognition gets more and more attention for its high accuracy rate. However, the iris images are often occluded by eyelids and eyelashes partly and if these noises can't be removed the performance of iris recognition system will be degraded badly. ...
An improved palmprint recognition system using iris features

This paper presents a bimodal biometric recognition system based on the extracted features of the human palmprint and iris using a new graph-based approach termed Fisher locality preserving projections (FLPP). This new technique employs two graphs with ...
Iris Recognition: An Entropy-Based Coding Strategy Robust to Noisy Imaging Environments
Advances in Visual Computing
Abstract
The iris is currently accepted as one of the most accurate traits for biometric purposes. However, for the sake of accuracy, iris recognition systems rely on good quality images and significantly deteriorate their results when images contain large ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

MoMM 2015: Proceedings of the 13th International Conference on Advances in Mobile Computing and Multimedia

December 2015

422 pages

ISBN:9781450334938

DOI:10.1145/2837126

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Johannes Kepler University, Linz, Austria
@WAS: International Organization of Information Integration and Web-based Applications and Services

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 December 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

MoMM 2015

MoMM 2015: The 13th International Conference on Advances in Mobile Computing and Multimedia

December 11 - 13, 2015

Brussels, Belgium

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
66
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Soni APatidar TM RP BS BRajendran R(2021)Iris Recognition using Hough Transform and Neural Architecture Search Network2021 Innovations in Power and Advanced Computing Technologies (i-PACT)10.1109/i-PACT52855.2021.9696917(1-5)Online publication date: 27-Nov-2021
https://doi.org/10.1109/i-PACT52855.2021.9696917
Mei W(2019)Probability/Possibility Systems for Modeling of Random/Fuzzy Information with Parallelization ConsiderationInternational Journal of Fuzzy Systems10.1007/s40815-019-00627-9Online publication date: 2-Apr-2019
https://doi.org/10.1007/s40815-019-00627-9
Bellaaj MBoukhris RDamak ASellami D(2016)Possibilistic modeling palmprint and fingerprint based multimodal biometric recognition system2016 International Image Processing, Applications and Systems (IPAS)10.1109/IPAS.2016.7880147(1-8)Online publication date: Nov-2016
https://doi.org/10.1109/IPAS.2016.7880147

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten