Performance Analysis of Feature Extraction and its Fusion Techniques for Iris Recognition System


Performance Analysis of Feature Extraction and its Fusion Techniques for Iris Recognition System


Aiyeniko, O, Adekunle, Y.A , Eze, M.O., Alao, O.D, *Ebiesuwa Seun

Department of Computer Science, Babcock University


Global Journal of Artificial Intelligence

The extraction of feature shows a significant part of iris recognition system. The robustness of recognition accuracy mostly depends on efficient extraction of feature. In the development of an effective recognition system, it is required that the best discriminating feature available in an iris pattern to be properly extracted. This paper applied some selected feature extraction techniques: 1D Log-Gabor Filter (1D LGF), 2-D Gabor-Filter (2D GF), Discrete Cosine Tansform (DCT) and Scale Invariant Feature Transform (SIFT) for extraction of iris features and fusion technique. The CASIA iris image dataset was used to evaluate with evaluation parameters: False Acceptance Rate (FAR), False Rejection Rate (FRR), Error Rate (RA) and Recognition Accuracy (RA). The combined 1D Log-Gabor and 2D Gabor filter approach outperformed other techniques with 92.22% of recognition accuracy, FRR of 0.0186, FAR of 0.1052 and ER of 2.87%.


Keywords: Feature Extraction, Iris Recognition, 1-D Log Gabor Filter, 2-D Gabor Wavelet Transform, Scale Invariant Feature Transform

Free Full-text PDF


How to cite this article:
Aiyeniko, O, Adekunle, Y.A , Eze, M.O., Alao, O.D, Ebiesuwa Seu.Performance Analysis of Feature Extraction and its Fusion Techniques for Iris Recognition System. Global Journal of Artificial Intelligence, 2020; 2:7.


References:

1. T. Thomas, A. George, and K. P. I. Devi, “Effective Iris Recognition System,” Procedia Technol., vol. 25, pp. 464–472, 2016.
2. Yi Chen, S. Dass, A. Ross, and A. Jain, “Fingerprint Deformation Models Using Minutiae Locations and Orientations,” in 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION’05) – Volume 1, 2005, pp. 150–155.
3. N. J. Pithadia and V. D. Nimavat, “A Review on Feature Extraction Techniques,” Int. J. Eng. Res. Technmology, vol. 1, no. 3, pp. 1263–1268, 2015.
4. J. Daugman, “How Iris Recognition Works,” Essent. Guid. to Image Process., vol. 14, no. 1, pp. 715–739, 2009.
5. S. A. Adegoke, B. O, Omidiora, E. O, Ojo, J. A., & Falohun, “Iris feature extraction : A survey,” Comput. Eng. Intell., vol. 4, no. 9, pp. 7–14, 2013.
6. C. C. Tsai, J. S. Taur, and C. W. Tao, “Iris recognition using gabor filters optimized by the particle swarm technique,” Conf. Proc. – IEEE Int. Conf. Syst. Man Cybern., vol. 18, no. 2, pp. 921–926, 2008.
7. S. R. Khaladkar, M., & Ganorkar, “Comparative Analysis for Iris Recognition,” Int. J. Eng. Rsearch Technol., vol. 1, no. 4, pp. 1–6, 2012.
8. N. Feddaoui and K. Mahersia, H., & Hamrouni, “Iris Recognition Method Based on Gabor Filters and Uniform Local Binary Patterns,” Int. J. Image Graph., vol. 12, no. 02, p. 1250014, 2012.
9. M. R. Mude, R & Patel, “Gabor Filter for Accurate IRIS Segmentation Analysis,” Int. J. Innov. Eng. Technol., vol. 6, no. 1, pp. 148–153, 2015.
10. P. Mukherjee, A. Dutta, and P. Das, “An Effective Method for Iris Recognition Based on Discrete Cosine Transform,” Int. J. Comput. Sci. Inf. Technol. Secur., vol. 4, no. 2, pp. 50–54, 2014.
11. K. M. Gandhi and P. R. H. Kulkarni, “Sift Algorithm for Iris Feature Extraction,” Glob. J. Comput. Sci. Technol. Graph. Vis., vol. 14, no. 3, pp. 0–6, 2014.
12. V. Saini, N & Kang, “Comparative Analysis of Iris Recognition Techniques : A Review,” Int. J. Comput. Appl., vol. 2, no. 7, pp. 23–27, 2016.
13. H. A. Biu, R. Husain, and A. S. Magaji, “An Enhanced Iris Recognition and Authentication System Using Energy Measure,” Sci. World J., vol. 13, no. 1, pp. 11–17, 2018.
14. K. Devi, P. Gupta, D. Grover, and A. Dhindsa, “An Effective Feature Extraction Approach for Iris Recognition System,” Indian J. Sci. Technol., vol. 9, no. December, pp. 1–5, 2016.
15. A. Bansal, R. Agarwal, and R. K. Sharma, “Statistical feature extraction based iris recognition system,” Sadhana, vol. 41, no. 5, pp. 507–518, 2016.
16. R. Harsha and K. Ramesha, “DWT Based Feature Extraction for Iris Recognition,” Int. J. Adv. Reserach Comput. Commun. Eng., vol. 4, no. 5, pp. 300–306, 2015.
17. S. J. Kerim, A. A & Mohammed, “New Iris Feature Extraction and Pattern Matching Based on Statistical Measurement,” vol. 3, no. 5, pp. 226–231, 2014.