Published February 28, 2025 | Version CC-BY-NC-ND 4.0

Real-Time Iris Detection and Recognition System Using You Only Look Once Version 8

  • 1. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai (Tamil Nadu), India.

Contributors

Contact person:

  • 1. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai (Tamil Nadu), India.

Description

Abstract: The model which is used in real time object detection which has high speed and accuracy which processes the images in a single pass is the You Look Only Once model. This project mainly the focus on the application of YOLOv8 or You Look Only Once version 8 model for iris detection and recognition in biometric systems, focusing on high-security and accuracy. to improve the performance of model under various lighting conditions it was trained under various customized datasets. To improve the generalization of the model advanced image augmentation techniques like flips, rotation and brightness adjustments were done . The model yielded 95% average precision on the validation set which was trained using pytorch framework with optimized hyperparameters which shows the effectiveness of YOLOv8 in real time iris recognition and detection.

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Dates

Accepted
2025-02-15
Manuscript received on 30 November 2024 | First Revised Manuscript received on 11 December 2024 | Second Revised Manuscript received on 25 January 2025 | Manuscript Accepted on 15 February 2025 | Manuscript published on 28 February 2025.

References

  • D. P. Benalcazar, D. A. Benalcazar and A. Valenzuela, "Artificial Pupil Dilation for Data Augmentation in Iris Semantic Segmentation," 2022 IEEE Sixth Ecuador Technical Chapters Meeting (ETCM), Quito, Ecuador, 2022. DOI: http://dx.doi.org/10.1109/ETCM56276.2022.9935749
  • Al-Waist, A.S., Qahwaji, R., Ipson, S. et al. A multi- biometric iris recognition system based on a deep learning approach. Pattern Anal Applic 21, 783–802 (2018). DOI: http://dx.doi.org/10.1007/s10044- 017-0656-1
  • Tann, Hokchhay, Heng Zhao, and Sherief Reda. "A resource-efficient embedded iris recognition system using fully convolutional networks." ACM Journal on Emerging Technologies in Computing Systems (JETC) 16.1 (2019). DOI: http://dx.doi.org/10.48550/arXiv.1909.03385
  • Choudhary, M., Tiwari, V. & Uduthalapally, V. Iris presentation attack detection based on best-k feature selection from YOLO inspired RoI. Neural Comput & Applic 33, 5609– 5629 (2021). DOI: https://doi.org/10.1007/s00521-020-05342-3
  • S. He and X. Li, "EnhanceDeepIris Model for Iris Recognition Applications," in IEEE Access, vol. 12, pp. 66809- 66821, 2024. DOI: https://dx.doi.org/10.1109/ACCESS.2024.338816
  • D. R. Lucio, R. Laroca, L. A. Zanlorenzi, G. Moreira and Menotti, "Simultaneous Iris and Periocular Region Detection Using Coarse Annotations," 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Rio de Janeiro, Brazil, 2019. DOI: http://dx.doi.org/10.48550/arXiv.1908.00069
  • Ahmad, Naseem, Muhammad Ghulam, Kuldeep Singh Yadav, Rabul Hussain Laskar, Ashraf Hossain and Zulfiqar Ali. "A cascaded deep learning framework for iris center localization in facial image(2023). DOI: http://dx.doi.org/10.1111/exsy.13483
  • Kimura, Gabriela Y., Diego R. Lucio, Alceu S. Britto Jr, and David Menotti. "CNN hyperparameter tuning applied to iris liveness detection." arXiv preprint arXiv:2003.00833 (2020). DOI: http://dx.doi.org/10.48550/arXiv.2003.00833
  • Alay, N.; Al-Baity, H.H. Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits. Sensors 2020. DOI: http://dx.doi.org/10.3390/s20195523
  • Minaee, Shervin, and Amirali Abdolrashidi. "Deepiris: Iris recognition using a deep learning approach." arXiv preprint arXiv:1907.09380 (2019). DOI: http://dx.doi.org/10.48550/arXiv.1907.09380
  • A. Kuehlkamp, A. Pinto, A. Rocha, K. W. Bowyer and Czajka, "Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection," in IEEE Transactions on Information Forensics and Security, vol. 14, no. 6, pp. 1419-1431, June 2019. DOI: http://dx.doi.org/10.1109/TIFS.2018.2878542
  • Hamd, Muthana H. and Samah K. Ahmed. "Biometric System Design for Iris Recognition Using Intelligent Algorithms." International Journal of Modern Education and Computer Science 10 (2018). DOI: http://dx.doi.org/10.5815/ijmecs.2018.03.02
  • Ahmad, Naseem, Kuldeep Singh Yadav, Anish Monsley Kirupakaran, Saharul Alom Barlaskar, Rabul Hussain Laskar and Ashraf Hossain. "Design and development of an integrated approach towards detection and tracking of iris using deep learning." Multim. Tools Appl. 83 (2023). DOI: http://dx.doi.org/10.1007/s11042-023-17433-z
  • J. E. Zambrano, J. I. Pilataxi, C. A. Perez and K. W. Bowyer, "Iris Recognition Using an Enhanced Pre-Trained Backbone Based on Anti-Aliased CNNs," in IEEE Access, vol. 12, pp. 94570-94583, 2024. DOI: http://dx.doi.org/10.1109/ACCESS.2024.3425648
  • L. Wang, K. Zhang, M. Ren, Y. Wang and Z. Sun, "Recognition Oriented Iris Image Quality Assessment in the Feature Space," 2020 IEEE International Joint Conference on Biometrics (IJCB), Houston, TX, USA, 2020. DOI: http://dx.doi.org/10.1109/IJCB48548.2020.9304896
  • S. Ahmad and B. Fuller, "ThirdEye: Triplet Based Iris Recognition without Normalization," 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS), Tampa, FL, USA, 2019. DOI: http://dx.doi.org/10.1109/BTAS46853.2019.9185998
  • D. Kerrigan, M. Trokielewicz, A. Czajka and K. W. Bowyer, "Iris Recognition with Image Segmentation Employing Retrained Off-theShelf Deep Neural Networks," 2019 International Conference on Biometrics (ICB), Crete, Greece, 2019. DOI: http://dx.doi.org/10.1016/j.imavis.2019.103866
  • Tapia, Juan E., Sebastian Gonzalez, Daniel Benalcazar, and Christoph Busch. "On the Feasibility of Creating Iris Periocular Morphed Images." arXiv preprint arXiv:2408.13496 (2024). DOI: http://dx.doi.org/10.48550/arXiv.2408.13496
  • Fang, Zhaoyuan, and Adam Czajka. "Open source iris recognition hardware and software with presentation attack detection." 2020 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2020. DOI: http://dx.doi.org/10.1109/IJCB48548.2020.9304869
  • S. Senthil Pandi, V. R. Chiranjeevi, K. T and Kumar P, Improvement of Classification Accuracy in Machine Learning Algorithm by HyperParameter Optimization(2023), 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), Chennai, India, 2023. DOI: http://dx.doi.org/10.1109/RMKMATE59243.2023.10369177
  • Kumar P, V. K. S, P. L and S. SenthilPandi(2023), "Enhancing Face Mask Detection Using Data Augmentation Techniques," 2023 International Conference on Recent Advances in Science and Engineering Technology (ICRASET), B G NAGARA, India, 2023, pp. 1-5, DOI: https://doi.org/10.1109/ICRASET59632.2023.10420361.
  • R. K. Mahendran, R. Aishwarya, R. Abinayapriya and P. Kumar, "Deep Transfer Learning Based Diagnosis of Multiple Neurodegenerative Disorders," 2024 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2024. DOI: https://doi.org/10.1109/ESCI59607.2024.10497320
  • A., O., & O, B. (2020). An Iris Recognition and Detection System Implementation. In International Journal of Inventive Engineering and Sciences (Vol. 5, Issue 8, pp. 8–10). DOI: https://doi.org/10.35940/ijies.h0958.025820
  • Harini, K., Yamuna, Dr. G., & Santhiya, V. (2020). Biometric Iris Recognition System using Multiscale Feature Extraction Method. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 6, pp. 2298–2303). DOI: https://doi.org/10.35940/ijrte.f8016.038620
  • Bankar, R., & Salankar, S. (2020). Face Tracking Performance in Head Gesture Recognition System. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 5, pp. 1096– 1099). DOI: https://doi.org/10.35940/ijeat.e1043.069520
  • Singh, S., Gupta, A. K., & Singh, T. (2019). Sign Language Recognition using Hybrid Neural Networks. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 2, pp. 1092–1098). DOI: https://doi.org/10.35940/ijitee.l3349.129219
  • Rathore, R., & Shrivastava, Dr. N. (2023). Network Anomaly Detection System using Deep Learning with Feature Selection Through PSO. In International Journal of Emerging Science and Engineering (Vol. 11, Issue 5, pp. 1–6). DOI: https://doi.org/10.35940/ijese.f2531.0411523