Segmentation Approach for a Noisy Iris Images Based on Hybrid Techniques
Engineering and Technology Journal,
2020, Volume 38, Issue 11, Pages 1684-1691
AbstractIris recognition indicates the procedure of recognizing humans based on their both left and right iris patterns. Nowadays there is rapid progress in realizing an old dream of developing a user-friendly recognition system. Most of the new projects became a nightmare of security of the system. The prosperity of iris recognition aside from its attractive physical characteristics is led to developing an efficient feature extractor to attain the required objective of recognition. Fingerprint, facial, and iris biometric techniques are developed widely for identifying processing most boarded management points, access control, and military checkpoints. Hybridization between Daugman’s Integro Differential Operator (IDO) with edge base methods was realized through taking the advantages of the good qualities of both methods so as to enhance the precision and reduce the required time. The proposed hybrid recognition system is very reliable and accurate. UBIRIS version 1 dataset was utilized in the conducted simulation which indicates the distinctions of the hybrid method in providing good performance and accuracy with reducing the time consuming of iris localization by approximately 99% compared with IDO and edge based methods.
 E. Severo, R. Laroca, C. Bezerra and L. A. Zanlorensi, “A Benchmark for Iris Location and a Deep Learning Detector Evaluation,” International Joint Conference on Neural Networks (IJCNN) 2018, Rio, Brazil, 2018.
 A. Hashim and D. Noori, "An Approach of Noisy Color Iris Segmentation Based on Hybrid Image Processing Techniques," 2016 International Conference on Cyberworlds (CW), Chongqing, 2016, pp. 183-188.
 H. K. Rana, MS Azam, MR Akhtar, Quinn JMW, Moni MA. “A Fast Iris Recognition System through Optimum Feature Extraction,” PeerJ Computer Science, Vol. 10, No. 2, pp.1-10, 2019.
 R.P., Wildes, “Iris Recognition: An Emerging Biometric Technology,” Proceedings of the IEEE, Vol. 85, No. 9, pp.1348-1363, 1997.
 L. Masek, "Recognition of Human Iris Patterns For Biometric Identification", B.Sc. Thesis, the School of Computer Science and Software Engineering, The University of Western Australia, 2003.
 Y. Chen, M. Adjouadi, C. Han, J. Wang, A. Barreto, N. Rishe, and J. Andrian, "A Highly Accurate and Computationally Efficient Approach For Unconstrained Iris Segmentation," Image and Vision Computing, Vol. 28, No.2, pp.261-269, 2010.
 L.L. Ling, and D.F. de Brito, "Fast and Efficient Iris Image Segmentation," Journal of Medical and Biological Engineering, Vol. 30, No. 6, pp. 381-392, 2010.
 A. Uhl, and P. Wild, "Weighted Adaptive Hough and Ellipsopolar Transforms For Real-Time Iris Segmentation," In Proceedings of the 5th IEEE International Conference on Biometrics, New Delhi, India, pp. 283-290, 29 March–1 April 2012.
 J. J. Fernandez, and A. Mathew, "Irregular Pupil Localization Using Connected Component Analysis," Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013 International Multi-Conference on, pp. 155-159, 2013.
 Z. Zhao, and A. Kumar, "An Accurate Iris Segmentation Framework Under Relaxed Imaging Constraints Using Total Variation Model,” In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, pp. 3828-3836, 7-13 December 2015.
 Z. Li, "An Iris Recognition Algorithm Based on Coarse and Fine Location," Big Data Analysis (ICBDA), 2017 IEEE 2nd International Conference, pp. 744-747, 2017.
 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, 1993.
 Z. Yu and W. Cui, "A Rapid Iris Location Algorithm Based on Embedded," International Conference on Computer Science and Information Processing (CSIP), pp. 233-236, Xi'an, Shaanxi, 2012.
 A. Radman, K. Jumari, and N. Zainal, "Fast and Reliable Iris Segmentation Algorithm," IET Image Processing, Vol. 7, No. 1, pp.42-49, 2013.
 A. T. Hashim, Duaa A. Noori, "An Approach of Noisy Color Iris Segmentation Based on Hybrid Image Processing Techniques," International Conference on Cyberworlds (CW), 2016.
 A. Hashim and Zina Saleh, “Fast Iris Localization Based on Image Algebra and Morphological Operations,” Journal of University of Babylon for Pure and Applied Sciences, Vol. 27, No. 2, 2019.
 P. R. Wildes, “Iris Recognition: An Emerging Biometric Technology, “In Proceedings of the IEEE, Vol. 85, No.9, pp. 1348-1363, U.S.A., 1997.
 L. Liam, A. Chekima, L. Fan, and J. dargham, “Iris Recognition Using Self-Organizing Neural Network”, In IEEE, 2002 Student Conference on Research and Developing Systems, pp. 169-172, Malaysia, 2002.
 S. A. Ali, ”Irregular Iris Identification and Verification using Texture Methods”, Ph.D. Thesis, Babylon University, Iraq, 2014.
- Article View: 12
- PDF Download: 1