Improvement of iris recognition technology for biometric identification of a person
DOI:
https://doi.org/10.15587/1729-4061.2022.269948Keywords:
pattern recognition, segmentation method, iris recognition technology, biometric personality authenticationAbstract
This topic is very relevant in the field of artificial intelligence as a direction of pattern recognition. In this work, the iris of the eye is considered as an image.
Artificial intelligence makes this technology more accessible for use in CCTV cameras, smartphones and various areas of human activity.
The article reflects the results of a study of methods and technologies of pattern recognition on the example of the human iris.
The aim of the work was to study methods and technologies for human iris recognition and iris recognition of employees of a particular organization using EyeLock equipment by comparing segmentation results with Daugman standard segmentation.
Comparison analysis of segmentation results with standard segmentation can be done by directly measuring the number of correctly segmented irises in both methods, or by indirectly measuring the effect of segmentation on iris recognition performance. The method using the Daugman integral-differential operator has the greatest efficiency. The performance of the neural network has been improved. To use a neural network to classify iris profiles, we selected sets of images (images per person) as training images, and the rest of the images were used as test images. Training time (in seconds): for the Daugman method 170.7, and for the parabolic method 204.7.
The Daugman integro-differential operator is applied to the captured image to obtain the "maximum integral derivative of the contour" with ever-increasing radius on "successively decreasing scales" in three parameters: center coordinates and radius. Finding the maximum when the search coordinates deviate along an unwinding spiral.
Methods and techniques for pattern recognition have been investigated using the human iris
References
- Birajadar, P., Haria, M., Gadre, V. (2022). Scattering Wavelet Network-Based Iris Classification: An Approach to De-duplication. Smart Innovation, Systems and Technologies, 705–718. doi: https://doi.org/10.1007/978-981-19-3571-8_64
- Vyas, R., Kanumuri, T., Sheoran, G., Dubey, P. (2021). Accurate feature extraction for multimodal biometrics combining iris and palmprint. Journal of Ambient Intelligence and Humanized Computing, 13 (12), 5581–5589. doi: https://doi.org/10.1007/s12652-021-03190-0
- Huo, G., Lin, D., Yuan, M. (2022). Iris segmentation method based on improved UNet++. Multimedia Tools and Applications, 81 (28), 41249–41269. doi: https://doi.org/10.1007/s11042-022-13198-z
- Jain, A. K., Deb, D., Engelsma, J. J. (2022). Biometrics: Trust, But Verify. IEEE Transactions on Biometrics, Behavior, and Identity Science, 4 (3), 303–323. doi: https://doi.org/10.1109/tbiom.2021.3115465
- Hrytsyk, V., Nazarkevych, M. (2021). Real-Time Sensing, Reasoning and Adaptation for Computer Vision Systems. Lecture Notes on Data Engineering and Communications Technologies, 573–585. doi: https://doi.org/10.1007/978-3-030-82014-5_39
- Al Shalchi, N. F. A., Rahebi, J. (2022). Human retinal optic disc detection with grasshopper optimization algorithm. Multimedia Tools and Applications, 81 (17), 24937–24955. doi: https://doi.org/10.1007/s11042-022-12838-8
- Ju, L., Wang, X., Wang, L., Mahapatra, D., Zhao, X., Zhou, Q. et al. (2022). Improving Medical Images Classification With Label Noise Using Dual-Uncertainty Estimation. IEEE Transactions on Medical Imaging, 41 (6), 1533–1546. doi: https://doi.org/10.1109/tmi.2022.3141425
- Pavel'eva, E. A., Krylov, A. S., Ushmaev, O. S. (2009). Razvitie informatsionnoy tekhnologii identifikatsii cheloveka po raduzhnoy obolochke glaza na osnove preobrazovaniya Ermita. Sistemy vysokoy dostupnosti, 5 (1), 36–42. Available at: https://elibrary.ru/item.asp?id=13070173
- Note on CASIA-IrisV3. Available at: http://www.cbsr.ia.ac.cn/IrisDatabase.htm
- Savel'eva, E. A., Krylov, A. S. (2008). Algoritmy predobrabotki izobrazheniy raduzhnoy obolochki glaza. Proceedings of the 18th International Conference on Computer Graphics and Vision GraphiCon'2008. Moscow, 314. Available at: https://imaging.cs.msu.ru/pub/IrisPreprocess08.pdf
- Daugman, J. G. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15 (11), 1148–1161. doi: https://doi.org/10.1109/34.244676
- Wildes, R. P. (1997). Iris recognition: an emerging biometric technology. Proceedings of the IEEE, 85 (9), 1348–1363. doi: https://doi.org/10.1109/5.628669
- Povkhan, I., Lupei, M. (2020). The Algorithmic Classification Trees. 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP). doi: https://doi.org/10.1109/dsmp47368.2020.9204198
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Aliya Kintonova, Igor Povkhan, Marzhan Mussaif, Galymzhan Gabdreshov
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.