Improvement of iris recognition technology for biometric identification of a person

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.269948

Keywords:

pattern recognition, segmentation method, iris recognition technology, biometric personality authentication

Abstract

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

Author Biographies

Aliya Kintonova, L. N. Gumilyov Eurasian National University

Candidate of Technical Sciences, Associate Professor

Department of Artificial Intelligence Technologies

Igor Povkhan, Uzhhorod National University

Doctor of Technical Sciences, Professor, Dean

Marzhan Mussaif, L. N. Gumilyov Eurasian National University

PhD Student

Department of Artificial Intelligence Technologies

Galymzhan Gabdreshov, Research Institute "Sezual"

Candidate of Pedagogical Sciences, Director

References

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. Note on CASIA-IrisV3. Available at: http://www.cbsr.ia.ac.cn/IrisDatabase.htm
  10. 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
  11. 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
  12. 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
  13. 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

2022-12-30

How to Cite

Kintonova, A., Povkhan, I., Mussaif, M., & Gabdreshov, G. (2022). Improvement of iris recognition technology for biometric identification of a person. Eastern-European Journal of Enterprise Technologies, 6(2 (120), 60–69. https://doi.org/10.15587/1729-4061.2022.269948