IRIS PATTERN RECOGNITION AS AN INFALLIBLE MEAN OF IDENTITY ESTABLISHMENT IN FORENSIC CASEWORK
Keywords:
identification, Iris identificationAbstract
Biometrics is a mean of forensic recognition based on measurements and calculations of human body or biological features. Human body features. Biometric identification systems may make use of iris, retina, fingerprints, face, ear, gait parameters, facial physiognomy, photogrammetric or geometry features, etc. for recognition and hence identification pursuits. The high security areas like airports, nuclear reactors, thermal or hydro-electric power plants, legislative buildings, defense establishments etc., require secured entry of authorized persons. Biometric tools of recognition like automatic face recognition system can play a crucial role in recognizing illegal intruders to such areas. Iris is an excellent trait to be used in biometric system for recognition of an individual due to its individualistic, unique, universal and inimitable features. In this article, the current national and international status of use of iris patterns for forensic recognition, its advantages and limitations, and future prospects have been presented.
References
1. S. Dalal, T. Sahoo, A selective feature matching approach for iris recognition, Int. J. Comput. Appl. 41 (20) (2012) 34-39.
2. S. Viriri, J. Tapamo, Iris pattern recognition based on cumulative sums and majority vote methods, Int. j. adv. robot. syst. 14 (3) (2017) 1-9.
3. A.K. Jain, A. Ross, S. Prabhakar, An introduction to biometric recognition, IEEE Trans. Circuits Syst. Video Technol. 14 (1) (2004) 4-20.
4. F. Monrose, A. D. Rubin, Keystroke dynamics as a biometric for authentication, Future Gener. Comput. Syst. 16 (4) (2000) 351-359.
5. H. Proenca, L.A. Alexandre, Toward non cooperative iris recognition: A classification approach using
multiple signatures, IEEE Trans. Pattern Anal. Mach. Intell. 29 (4) (2007) 607-612.
6. D. Bhattacharyya, R. Ranjan, F. Alisherov, M. Choi, Biometric authentication: A review, International Journal of u-and e-Service, Science and Technology 2 (3) (2009) 13-28.
7. Biometric authentication: what method works best? Available at: http://www.technovelgy.com/ct/ Technology-Article.asp?ArtNum=16. (Accessed 19 March 2018).
8. A. Ross, Iris recognition: The path forward, Computer 43 (2) (2010) 30-35.
9. H. Rai, A. Yadav, Iris recognition using combined support vector machine and Hamming distance approach, Expert Syst. Appl. 41 (2) (2014) 588-593.
10. S.S. Chowhan, G.N. Shinde, Iris biometrics recognition application in security management, 2008 Congress on Image and Signal Processing 1 (2008) 661-665.
11. Why governments are after biometrics to establish national ID database? Available at: https:// www.bayometric.com/biometrics-establish-national id-database. (Accessed 12 March 2018).
12. P. Verma, M. Dubey, P. Verma, and S. Basu, Daughman’s algorithm method for iris recognition—a biometric approach, Int. J. Adv. Res. Technol. 2 (6) (2012) 177-185.
13. Unique Identification Authority of India (UIDAI), Unique Identification numbers. Available at: https:// www.uidai.gov.in/about-uidai/about-uidai.html. (Accessed 10 March 2018).
14. J. Daugman, 600 Million Citizens of India are now enrolled with Biometric ID, SPIE newsroom 7 (2014).
15. K. Hajari, K. Bhoyar, A review of issues and challenges in designing Iris recognition Systems for noisy imaging environment, 2015 International Conference on Pervasive Computing (ICPC), IEEE, (2015) 1-6.
16. S. Dey, D. Samanta, A novel approach to iris localization for iris biometric processing, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering 1 (5) (2007) 293-304.
17. J. Daugman, The importance of being random: statistical principles of iris recognition, Pattern recognit. 36 (2) (2003) 279-291.
18. M. Dobes, L. Machala, P. Tichavsky, J. Pospisil, Human eye iris recognition using the mutual information, Optik 115 (9) (2004) 399-404.
19. Y. Chen, M. Adjouadi, A. Barreto, N. Rishe, J. Andrian, A computational efficient iris extraction approach in unconstrained environments, Biometrics: Theory, Applications, and Systems, 2009. BTAS’09. IEEE 3rd International Conference on. IEEE, (2009) 1-7.
20. Y. Chen, M. Adjouadi, C. Han, J. Wang, A. Barreto, N. Rishe, J. Andrian, A highly accurate and computationally efficient approach for unconstrained iris segmentation, Image Vis. Comput. 28 (2) (2010) 261-269.
21. S. Lagree, K.W. Bowyer, Predicting ethnicity and gender from iris texture, 2011 IEEE International Conference on Technologies for Homeland Security (HST), IEEE, (2011) 440-445.
22. P. Li, X. Liu, N. Zhao, Weighted co-occurrence phase histogram for iris recognition, Pattern Recognit. Lett. 33 (8) (2012) 1000-1005.
23. J. Daugman, C. Downing, No change over time is shown in Rankin et al. Iris recognition failure over time, Pattern Recognit. 46 (2) (2013) 609-610.
24. M. De Marsico, C. Galdi, M. Nappi, D. Riccio, Firme: Face and iris recognition for mobile engagement, Image Vis. Comput. 32 (12) (2014) 1161-1172.
R.R. Jillela, A. Ross, Segmenting iris images in the visible spectrum with applications in mobile biometrics, Pattern Recognit. Lett. 57 (2015) 4-16.
D. Yambay, B. Becker, N. Kohli, D. Yadav, A. Czajka, K.W. Bowyer, S. Schuckers, R. Singh, M. Vatsa, A. Noore, D. Gragnaniello, LivDet iris 2017—Iris liveness detection competition 2017, IEEE International Joint Conference on Biometrics (IJCB), IEEE, (2017) 733- 741.
E.G. Llano, M.S. Vazquez, J.M. Vargas, L.M. Fuentes, A.A. Acosta, Optimized robust multi-sensor scheme
for simultaneous video and image iris recognition, Pattern Recognit. Lett. 101 (2018) 44-51.
28. N. Ahmadi, M. Nilashi, S. Samad, T.A. Rashid, H. Ahmadi, An intelligent method for iris recognition using supervised machine learning techniques, Opt. Laser Technol. (2019).
29. A. Dwivedi, A. Tolambiya, P. Kandula, N.S. Bose, A. Kumar, P.K. Kalra, Color image compression using 2- dimensional principal component analysis (2DPCA), Proc. of ASID ’06. 2 (2006) 488-491.
30. A. Kumar, A. Passi, Comparison and combination of iris matchers for reliable personal authentication, Pattern recognit. 43 (3) (2010) 1016-1026.
31. S. Umer, B.C. Dhara, B. Chanda, Iris recognition using multiscale morphologic features, Pattern Recognit. Lett. 65 (2015) 67-74.
32. M. Singh, S. Nagpal, M. Vatsa, R. Singh, A. Noore, A. Majumdar, Gender and Ethnicity Classification of Iris Images using Deep Class-Encoder, arXiv preprint arXiv: 1710.02856 (2017).
33. S.S. Barpanda, B. Majhi, P.K. Sa, A.K. Sangaiah, S. Bakshi, Iris feature extraction through wavelet mel frequency cepstrum coefficients, Opt. Laser Technol. (2018).
34. J.S. Sehrawat, D. Sankhyan. Iris patterns as a Biometric tool for Forensic Identifications: a review. Brazilian J Forensic Sciences, Medical Law and Bioethics, 2016; 5 (4):431-440