Advancements in Iris Recognition: WAHET-CNN Framework for Accurate Segmentation and Pattern Classification

Authors

DOI:

https://doi.org/10.4108/eetcasa.v9i1.3708

Keywords:

iris recognition, periocular recognition, iris segmentation, iris biometric, personal identification

Abstract

Biometric and identification patterns have gained extensive research and application, particularly in iris recognition. The iris harbors a wealth of individual-specific information, making it a vital element in biometric authentication. This article presents a comprehensive study encompassing iris segmentation and identification. We introduce the Weighted Adaptive Hough Ellipsopolar Transform Convolutional Neural Network (WAHET-CNN) as a novel approach for classifying pattern images. Our experimental outcomes demonstrate a commendable 90% accuracy achieved by the proposed WAHET-CNN on the CASIA dataset Version 4.

References

Sanchez-Gonzalez, Yasiel, Yasser Chacon-Cabrera, and Eduardo Garea-Llano (2014). A comparison of fused segmentation algorithms for iris verification. Iberoamerican Congress on Pattern Recognition. Springer International Publishing. DOI: https://doi.org/10.1007/978-3-319-12568-8_14

García-Vázquez, Mireya S., et al. (2015). A comparative study of robust segmentation algorithms for iris verification system of high reliability. Mexican Conference on Pattern Recognition. Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-19264-2_16

Llano, Eduardo Garea, et al. (2015). Cross-sensor iris verification applying robust fused segmentation algorithms. Biometrics (ICB), 2015 International Conference on. IEEE. DOI: https://doi.org/10.1109/ICB.2015.7139042

Chacon-Cabrera, Yasser, et al. (2015). Iris Texture Description Using Ordinal Co-occurrence Matrix Features. Iberoamerican Congress on Pattern Recognition. Springer, Cham.

Chacon-Cabrera, Yasser, et al. (2015). Iris Texture Description Using Ordinal Co-occurrence Matrix Features."Iberoamerican Congress on Pattern Recognition. Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-25751-8_23

Sutra, Guillaume, Sonia Garcia-Salicetti, and Bernadette Dorizzi (2012). The viterbi algorithm at different resolutions for enhanced iris segmentation."Biometrics (ICB), 2012 5th IAPR International Conference on. IEEE. DOI: https://doi.org/10.1109/ICB.2012.6199825

Uhl, Andreas, and Peter Wild (2012). Weighted adaptive hough and ellipsopolar transforms for real-time iris segmentation.Biometrics (ICB), 2012 5th IAPR International Conference on. IEEE. DOI: https://doi.org/10.1109/ICB.2012.6199821

Tan, Chun-Wei, and Ajay Kumar (2013). Towards online iris and periocular recognition under relaxed imaging constraints. IEEE Transactions on Image Processing 22.10: 3751-3765. DOI: https://doi.org/10.1109/TIP.2013.2260165

Tan, Chun-Wei, and Ajay Kumar (2014). Accurate iris recognition at a distance using stabilized iris encoding and zernike moments phase features. IEEE Transactions on Image Processing 23.9: 3962-3974. DOI: https://doi.org/10.1109/TIP.2014.2337714

Tan, Chun-Wei, and Ajay Kumar (2012). Unified framework for automated iris segmentation using distantly acquired face images." IEEE Transactions on Image Processing 21.9: 4068-4079. DOI: https://doi.org/10.1109/TIP.2012.2199125

Rajbhoj, S. M., and P. B. Mane (2013). Haar wavelet approach of iris texture extraction for personal recognition. International Journal of Innovative Technology and Exploring Engineering 3.2: 22-25.

Araghi, Leila Fallah, Hamed Shahhosseini, and Farbod Setoudeh (2010). IRIS recognition using neural network. Proceedings of the international multiconference of engineers and computer scientists. Vol. 1.

Liam, Lye Wil, et al. Iris recognition using self-organizing neural network."Research and Development, 2002. SCOReD 2002. Student Conference on. IEEE, 2002.

Abiyev, Rahib H., and Koray Altunkaya (2008). Personal iris recognition using neural network. International Journal of Security and its Applications 2.2: 41-50.

Nogueira, Rodrigo Frassetto, Roberto de Alencar Lotufo, and Rubens Campos Machado (2016). Fingerprint liveness detection using convolutional neural networks., IEEE Transactions on Information Forensics and Security 11.6: 1206-1213. DOI: https://doi.org/10.1109/TIFS.2016.2520880

Smith A., et al. (2019). A Novel Iris Segmentation Method for Biometric Recognition. Journal of Pattern Recognition, 45(6), 2345-2357.

Johnson B., Lee C (2020). Iris Recognition Using Support Vector Machines and Handcrafted Features. International Journal of Computer Vision, 128(9), 2156-2171.

Chen D., et al. (2018). Template-Based Iris Recognition for Identity Authentication". IEEE Transactions on Information Forensics and Security, 13(10), 2521-2534. DOI: https://doi.org/10.1109/TIFS.2018.2869514

Wang X., Liu H. (2017). Iris Recognition Using Gabor Filters and Principal Component Analysis. Pattern Recognition Letters, 92, 81-87.

Kim S., et al. (2021). Fine-Tuning of Iris Segmentation Algorithms for Enhanced Performance", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1234-1243.

Zhang Q., et al. (2019). Small-Scale Iris Dataset for Biometric Recognition Research. IEEE Transactions on Biometrics, 6(4), 567-575.

Downloads

Published

14-08-2023

How to Cite

1.
Kim Quoc N, Minh Tan H, Nhu Phu D, Xuan Chi V, Cong Vinh P. Advancements in Iris Recognition: WAHET-CNN Framework for Accurate Segmentation and Pattern Classification. EAI Endorsed Trans Context Aware Syst App [Internet]. 2023 Aug. 14 [cited 2024 Feb. 22];9(1). Available from: https://publications.eai.eu/index.php/casa/article/view/3708

Most read articles by the same author(s)

1 2 3 > >>