Iris Recognition Scheme Based on Entropy and Convolutional Neural Network
DOI:
https://doi.org/10.46604/aiti.2024.13516Keywords:
iris, segmentation, morphology, entropy, CNNAbstract
This study presents an advanced iris image segmentation approach to overcome vibration and occlusion from the lashes. The proposed scheme removes the surrounding areas of the iris image to recover the region of interest (ROI) containing the iris images. The entropy function and mathematical morphology are employed as the foundation of the proposed scheme. Initially, the entropy function is applied to the binarization image. Subsequently, the ROI is cropped and extracted from the binary image using the dilation method. Furthermore, a convolutional neural network (CNN) is used in the recognition phase. The database of the Indian Institute of Technology Delhi (IIT Delhi) serves as a test. The results yield a high level of accuracy—up to 93% during segmentation. Using half of the dataset during the recognition phase results in an accuracy of 98.8%, while using the complete database produces an accuracy of 97.5%.
References
I. S. Hussein, S. B. Sahibuddin, M. J. Nordin, and N. N. B. A. Sjarif, “Multimodal Recognition System Based on High-Resolution Palmprints,” IEEE Access, vol. 8, pp. 56113-56123, 2020.
G. Huo, D. Lin, and M. Yuan, “Multi-Source Heterogeneous Iris Segmentation Method Based on Lightweight Convolutional Neural Network,” IET Image Processing, vol. 17, no. 1, pp. 118-131, January 2023.
S. K. Balasubramanian, V. Jeganathan, and T. Subramani, “Deep Learning-Based Iris Segmentation Algorithm for Effective Iris Recognition System,” Proceedings of Engineering and Technology Innovation, vol. 23, pp. 60-70, January 2023.
M. Mostofa, S. Mohamadi, J. Dawson, and N. M. Nasrabadi, “Deep Gan-Based Cross-Spectral Cross-Resolution Iris Recognition,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3, no. 4, pp. 443-463, October 2021.
J. G. Daugman, “High Confidence Visual Recognition of Persons by a Test of Statistical Independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148-1161, November 1993.
J. Daugman, “How Iris Recognition Works,” The Essential Guide to Image Processing, 2nd ed., Amsterdam: Academic Press, pp. 715-739, 2009.
C. S. Hsiao, C. P. Fan, and Y. T. Hwang, “Design and Analysis of Deep-Learning Based Iris Recognition Technologies by Combination of U-Net and EfficientNet,” 9th International Conference on Information and Education Technology, pp. 433-437, March 2021.
X. Nie, H. Wang, B. Chai, and M. Duan, “Remote Sensing Image Instance Segmentation Based on Attention Balanced Feature Pyramid,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 37, no. 1, article no. 2254020, January 2023.
H. D. Rafik and M. Boubaker, “Application of Metaheuristic for Optimization of Iris Image Segmentation by Using Evaluation Hough Transform and Methods Daugman,” 1st International Conference on Communications, Control Systems and Signal Processing, pp. 142-150, May 2020.
S. Rapaka, P. Rajesh Kumar, M. Katta, K. Lakshminarayana, and N. Bhupesh Kumar, “A New Segmentation Method for Non-Ideal Iris Images Using Morphological Reconstruction FCM Based on Improved DSA,” SN Applied Sciences, vol. 3, no. 1, article no. 53, January 2021.
B. K. O. C. Alwawi and A. F. Y. Althabhawee, “Towards More Accurate and Efficient Human Iris Recognition Model Using Deep Learning Technology,” TELKOMNIKA Telecommunication Computing Electronics and Control, vol. 20, no. 4, pp. 817-824, August 2022.
M. Omran and E. N. AlShemmary, “An Iris Recognition System Using Deep Convolutional Neural Network,” Journal of Physics: Conference Series, vol. 1530, article no. 012159, 2020.
M. G. Alaslani and L. A. Elrefaei, “Convolutional Neural Network Based Feature Extraction for IRIS Recognition,” International Journal of Computer Science & Information Technology, vol. 10, no. 2, pp. 65-78, April 2018.
S. Minaee and A. Abdolrashidi, “Deepiris: Iris Recognition Using a Deep Learning Approach,” https://doi.org/10.48550/arXiv.1907.09380, July 22, 2019.
Z. Wang, C. Li, H. Shao, and J. Sun, “Eye Recognition with Mixed Convolutional and Residual Network (MiCoRe-Net),” IEEE Access, vol. 6, pp. 17905-17912, 2018.
M. S. Azam and H. K. Rana, “Iris Recognition Using Convolutional Neural Network,” International Journal of Computer Applications, vol. 175, no. 12, pp. 24-28, August 2020.
S. K. Tummala and I. Priyadarshini T, “Morphological Operations and Histogram Analysis of SEM Images Using Python,” Indian Journal of Engineering and Materials Sciences, vol. 29, no. 6, pp. 796-800, December 2022.
I. S. Hussein, S. B. Sahibuddin, M. J. Nordin, and N. N. Amir, “The Extract Region of Interest in High-Resolution Palmprint Using 2D Image Histogram Entropy Function,” Journal of Computer Science, vol. 15, no. 5, pp. 635-647, 2019.
R. Hermary, G. Tochon, É. Puybareau, A. Kirszenberg, and J. Angulo, “Learning Grayscale Mathematical Morphology with Smooth Morphological Layers,” Journal of Mathematical Imaging and Vision, vol. 64, no. 7, pp. 736-753, September 2022.
P. Kim, MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence, New York: Apress, pp. 121-147, 2017.
B. Moons, D. Bankman, and M. Verhelst, Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-on Neural Network Processing, Cham: Springer, 2019.
A. F. Gad, Practical Computer Vision Applications Using Deep Learning with CNNs: With detailed examples in Python using TensorFlow and Kivy, New York: Apress, 2018.
S. Minaee, A. Abdolrashidiy, and Y. Wang, “An Experimental Study of Deep Convolutional Features for Iris Recognition,” IEEE Signal Processing in Medicine and Biology Symposium, pp. 1-6, December 2016.
A. K. Pathak and A. Passi, “Comparison and Combination of Iris Matchers for Reliable Personal Identification,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, article no. 4563110, June 2008.
T. H. Mandeel, M. I. Ahmad, M. N. Md Isa, S. A. Anwar, and R. Ngadiran, “Palmprint Region of Interest Cropping Based on Moore-Neighbor Tracing Algorithm,” Sensing and Imaging, vol. 19, no. 1, article no. 15, December 2018.
I. S. Hussein and N. N. A. Sjarif, “Human Recognition Based on Multi-Instance Ear Scheme,” International Journal of Computing, vol. 22, no. 3, pp. 397-403, October 2023.
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