Evaluation of the Parameters Involved in the Iris Recognition System
الموضوعات :
1 - M. Tech. Research Scholar, Department of Computer Science and Engineering, Dr. B R Ambedkar National Institute of Technology, Jalandhar, India
الکلمات المفتاحية: Canny Edge Detector, Biometric Recognition, Equal Error Rate, Gamma Correction, Circle Hough Transform,
ملخص المقالة :
Biometric recognition is an automatic identification method which is based on unique features or characteristics possessed by human beings and Iris recognition has proved itself as one of the most reliable biometric methods available owing to the accuracy provided by its unique epigenetic patterns. The main steps in any iris recognition system are image acquisition, iris segmentation, iris normalization, feature extraction and features matching. EER (Equal Error Rate) metric is considered the best metric for evaluating an iris recognition system.In this paper, different parameters viz. the scaling factor to speed up the CHT (Circle Hough Transform), the sigma for blurring with Gaussian filter while detecting edges, the radius for weak edge suppression for the edge detector used during segmentation and the gamma correction factor for gamma correction; the central wavelength for convolving with Log-Gabor filter and the sigma upon central frequency during feature extraction have been thoroughly tested and evaluated over the CASIA-IrisV1 database to get an improved parameter set. This paper demonstrates how the parameters must be set to have an optimized Iris Recognition System.
1. A. Jain, L. Hong and S. Pankanti, Biometric Identification. Communications of the ACM 2000, 43(2), p. 91-98. DOI 10.1145/328236.328110.
2. Iris recognition. Available online: Wikipedia https://en.wikipedia.org/wiki/Iris_recognition (9 Aug 2018).
3. S. Sanderson and J. Erbetta. Authentication for secure environments based on iris scanning technology. IEEE Colloquium on Visual Biometrics, 2000.
4. K. W. Bowyer, K. Hollingsworth and P. J. Flynn. Image understanding for iris biometrics: A survey. Computer Vision and Image Understanding, May 2008, vol. 110, pp. 281-307.
5. J. Daugman. How iris recognition works. IEEE Trans. Circuits Syst. Video Technol., January 2004, vol. 14, pp. 21-30.
6. R. P. Wildes. Iris recognition: an emerging biometric technology. Proceedings of the IEEE, 1997, pp.1348-1363.
7. J. Daugman. Biometric personal identification system based on iris analysis. United States Patent, Patent Number: 5,291,560, 1994.
8. L. Masek. Recognition of Human Iris Patterns. Bachelor of Engineering degree, School of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Hwy, Crawley WA 6009, Australia, 2003.
9. M. Vatsa, R. Singh, A. Noore. Improving iris recognition performance using segmentation quality enhancement match score fusion and indexing. IEEE Trans. Syst. Man Cybern. B Cybern, 2008, vol. 38, no. 4, pp. 1021-1035.
10. T. Marciniak, A. Dąbrowski, A. Chmielewska, A. Krzykowska. Selection of parameters in iris recognition system. Multimedia Tools And Applications, January 2014, vol. 68, no. 1, pp. 193-208.
11. Rupesh Mude & Meenakshi R Patel. Gabor Filter for Accurate IRIS Segmentation Analysis. International Journal of Innovations in Engineering and Technology, October 2015, Volume 6 Issue 1.
12. Ajay Kumar, Arun Passi. Comparison and combination of iris matchers for reliable personal authentication. Pattern Recognition, March, 2010, vol. 43, no. 3, p.1016-1026, [DOI 10.1016/j.patcog.2009.08.016]
13. T. Tan, Note on CASIA-IrisV1, Chinese Academy of Sciences' Institute of Automation, 03 2011. Available online: http://biometrics.idealtest.org/. (Accessed April 30, 2018).
14. R. Wildes, J. Asmuth, G. Green, S. Hsu, R. Kolczynski, J. Matey and S. McBride. A system for automated iris recognition. Proceedings of the Second IEEE Workshop on Applications of Computer Vision, Sarasota, FL, 1994.
15. R. Hamming. Error detecting and error correcting codes. The Bell System Technical Journal, 1950, vol. 29, no. 2, pp. 147-160.
16. Thiyaneswaran B., Padma S. Analysis of Gabor Filter Parameter for Iris Feature Extraction. IJACT, 2014 Volume 3, Number 5. pp: 45-48.
17. A. S. Al-Waisy, R. Qahwaji, S. Ipson and S. Al- Fahdawi. A Fast and Accurate Iris Localization Technique for Healthcare Security System. IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015, pp. 1028–1034.
18. Identité numérique & authentification forte. Available online: https://fr.slideshare.net/smaret/identit-numrique-et-authentification-forte-1070073 (Accessed on 20, June 2018).
19. Canny edge detector. Available online: https://en.wikipedia.org/wiki/Canny_edge_detector. (Accessed on 30 April, 2018).
20. Gaussian Smoothing. Available online: https://homepages.inf.ed.ac.uk/rbf/HIPR2/gsmooth.htm (Accessed on 20 June, 2018).
21. Gamma correction. Available online: https://en.wikipedia.org/wiki/Gamma_correction. (Accessed on 30 April, 2018).
22. Log Gabor filter. Available online: https://en.wikipedia.org/wiki/Log_Gabor_filter. (Accessed on 30, April, 2018).
