Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter July 19, 2019

Iris recognition under the influence of diabetes

  • Mohammadreza Azimi , Seyed Ahmad Rasoulinejad EMAIL logo and Andrzej Pacut

Abstract

In this study, iris recognition under the influence of diabetes was investigated. A new database containing 1318 pictures from 343 irides – 546 images from 162 healthy irides (62% female users, 38% male users, 21% <20 years old, 61% (20) < 40 years old, 12% (40) <60 years old and 6% more than 60 years old) and 772 iris images from 181 diabetic eyes but with a clearly visible iris pattern (80% female users, 20% male users, 1% <20 years old, 17.5% (20) <40 years old, 46.5% (40) <60 years old and 35% more than 60 years old) – were collected. All of the diabetes-affected eyes had clearly visible iris patterns without any visible impairments and only type II diabetic patients with at least 2 years of being diabetic were considered for the investigation. Three different open source iris recognition codes and one commercial software development kit were used for achieving the iris recognition system’s performance evaluation results under the influence of diabetes. For statistical analysis, the t-test and the Kolmogorov-Simonov test were used.

Award Identifier / Grant number: 675087

Funding statement: This work is done with funding source from AMBER with sponsorship from the Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020, under Grant Agreement No. 675087, Funder Id: http://dx.doi.org/10.13039/100010661. The authors thank to all of participants who took part in this study.

  1. Author statement

  2. Research funding: Funding source from AMBER with sponsorship from the Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020, under Grant Agreement No. 675087.

  3. Conflict of interest: The authors have no conflict of interest to declare.

  4. Informed consent: Informed consent is not applicable.

  5. Ethical approval: The experiment was specifically designed to investigate if there is any relation between an iris recognition system’s accuracy and the health condition of the users or not. Before the experiment, consent agreements were signed by the study participants. The participants were also asked to provide non-biometric data, including their names, gender, age and the duration (if applies) of their diabetes illness. The personal data are kept separately to guarantee additional security of the personal data. As a result, all participants were fully aware of the experiment as detailed information on the study was provided.

References

[1] Azimi M, Pacut A. The effect of gender-specific facial expressions on face recognition systems relaibility, 2018, IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), 1–4. DOI: 10.1109/AQTR.2018.8402705.10.1109/AQTR.2018.8402705Search in Google Scholar

[2] Hollingsworth K, Bowyer KW, Lagree S, Fenker SP, Flynn PJ. Genetically identical irises have texture similarity that is not detected by iris biometrics. Comput Vis Image Underst 2011;115:1493–502.10.1016/j.cviu.2011.06.010Search in Google Scholar

[3] Daugman JG. High confidence visual recognition of persons by a test of statistical independence. IEEE T Pattern Anal 1993;15:1148–61.10.1109/34.244676Search in Google Scholar

[4] Howar JJ, Etter D. The Effect of Ethnicity, Gender, Eye Color and Wavelength on the Biometric Menagerie. IEEE International Conference on Technologies for Homeland Security (HST), 2013:627–632. DOI: 10.1109/THS.2013.6699077.Search in Google Scholar

[5] Rasoulinejad SA, Zarghami A, Hosseini SR, Rajaee N, Rasoulinejad SE, Mikaniki E. Prevalence of age-related macular degeneration among the elderly. Caspian J Intern Med 2015;6:141–7.Search in Google Scholar PubMed

[6] Rasoulinejad SA, Hajian-Tilaki K, Mehdipour E. Associated factors of diabetic retinopathy in patients that referred to teaching hospitals in Babol. Caspian J Intern Med 2015;6:224–8.Search in Google Scholar PubMed

[7] Noor-ul-huda M, Tehsin S, Ahmed S, Niazi FAK, Murtaza Z. Retinal images benchmark for the detection of diabetic retinopathy and clinically significant macular edema (CSME). Biomed Eng Biomed Tech 2019;64:297–307.10.1515/bmt-2018-0098Search in Google Scholar PubMed

[8] Samant P, Agarwal R. Machine learning techniques for medical diagnosis of diabetes using iris image. Comput Methods Programs Biomed 2018;157:121–8.10.1016/j.cmpb.2018.01.004Search in Google Scholar PubMed

[9] Heydari M, Teimouri M, Heshmati Z. Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran. Int J Diabetes Dev Ctries 2016;36:167–73.10.1007/s13410-015-0374-4Search in Google Scholar

[10] Chaksar UM, Sutaone MS. On a methodology for detecting diabetic presence from iris image analysis, 2012 International Conference on Power, Signals, Controls and Computation, 16. DOI: 10.1109/EPSCICON.2012.6175268.10.1109/EPSCICON.2012.6175268Search in Google Scholar

[11] Aslam TM, Tan SZ, Dhillon B. Iris recognition in the presence of ocular disease. J R Soc Interface 2009;6:489–93.10.1098/rsif.2008.0530Search in Google Scholar PubMed PubMed Central

[12] Seyeddain O, Kraker H, Redlbeger A, Dexl AK, Grabner G, Emesz M. Reliability of automatic biometric iris recognition after phacoemulsification or drug-induced pupil dilation. Eur J Ophthalmol 2014;24:58–62.10.5301/ejo.5000343Search in Google Scholar PubMed

[13] Borgen H, Bours P, Wolthusen SD. Simulating the influences of aging and ocular disease on biometric recognition performance. International Conference on Biometrics 2009. LNCS 2009; 5558:857–67.Search in Google Scholar

[14] Nigam I, Vatsa M, Singh R. Ophthalmic Disorder Menagerie and Iris Recognition. In: Bowyer K., Burge M, editors. Handbook of Iris Recognition. chapter 22. Advances in Computer Vision and Pattern Recognition. London: Springer; 2016:519–39.10.1007/978-1-4471-6784-6_22Search in Google Scholar

[15] Trokielewicz M, Czajka A, Maciejewicz P. Database of iris images acquired in the presence of ocular pathologies and assessment of iris recognition reliability for disease-affected eyes. IEEE 2nd International Conference on Strony. 2015:495–500.10.1109/CYBConf.2015.7175984Search in Google Scholar

[16] Trokielewicz M, Czajka A, Maciejewicz P. Implications of ocular pathologies for iris recognition reliability. Image Vision Comput 2017;58:158–67.10.1016/j.imavis.2016.08.001Search in Google Scholar

[17] Dhir L, Habib NE, Monro DM, Rakshit S. Effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication. Eye 2010;24:1006–10.10.1038/eye.2009.275Search in Google Scholar PubMed

[18] Available from: http://www.iritech.com/products/hardware/irishield%E2%84%A2-series.Search in Google Scholar

[19] Uhl A, Wild P. Weighted adaptive hough and ellipsopolar transforms for real-time iris segmentation. 5th International Conference on Biometrics (ICB’12). 2012:283–90.10.1109/ICB.2012.6199821Search in Google Scholar

[20] Zhang D, Monro DM, Rakshit S. DCT-based iris recognition. IEEE Trans Pattern Anal 2007;29:586–95.10.1109/TPAMI.2007.1002Search in Google Scholar PubMed

[21] Masek L, Kovesi P. MATLAB Source Code for a Biometric Identification System Based on Iris Patterns. The School of Computer Science and Software Engineering, The University of Western Australia, Perth, 2003.Search in Google Scholar

[22] Rathgeb C, Uhl A. Secure Iris Recognition based on Local Intensity Variations. In: Proceedings of the International Conference on Image Analysis and Recognition (ICIAR’10). Springer, LNCS 6112; 2010:266–75.10.1007/978-3-642-13775-4_27Search in Google Scholar

[23] Rathgeb C, Uhl A, Wild P, Hofbauer H. Design Decisions for an Iris Recognition SDK. In: Bowyer K, Burge M, editors. Handbook of Iris Recognition. Advances in Computer Vision and Pattern Recognition. London: Springer; 2016.10.1007/978-1-4471-6784-6_16Search in Google Scholar

[24] Available from: www.neurotechnology.com/verieye.html.Search in Google Scholar

Received: 2018-09-27
Accepted: 2018-12-21
Published Online: 2019-07-19
Published in Print: 2019-12-18

©2019 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 28.3.2024 from https://www.degruyter.com/document/doi/10.1515/bmt-2018-0190/html
Scroll to top button