Abstract
With the extensive use of biometric systems at most of the places for authentication, there are security and privacy issues concerning with it. Most of the public places we usually visit are under reconnaissance and may keep track of face, fingerprints, and iris etc. Thus, biometric information is not secret anymore and sensitive to spoofing attacks. Therefore, not only biometric traits but also liveness detection must be deployed in an authentication mechanism which is a challenging task. Iris is the most accurate trait and increasingly in demand in applications like national security, duplicate-free voter registration list, and Aadhar program its detection must be made robust. Solution to the iris liveness detection by extracting distinctive textural features from genuine (live) and fake (print) patterns using statistical approaches GLCM and GLRLM are implemented. Popular supervised SVM algorithm and PatternNet neural network with second-order scaled conjugate gradient training algorithm are assessed. Both of these algorithms are found to be faster with PatternNet outperforms over SVM.
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References
Gragnaniello D, Poggi G, Verdoliva L (2015) An investigation of local descriptors for biometric spoofing detection. IEEE Trans Inf Forensics Secur 10(4):849–863
Unar JA, Seng WC, Abbasi A (2014) A review of biometric technology along with trends and prospects. Pattern Recognit 47(8):2673–2688
Daugman John (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell 15(11):1148–1161
Daugman John (2004) How iris recognition works. IEEE Trans Circuits Syst Video Technol 14(1):21–30
He X, An S, Shi P Statistical texture analysis-based approach for fake iris detection using support vector machines. In: Proceedings of international conference on biometrics, pp 540–546
Lee E, Park K, Kim J (2005) Fake iris detection by using purkinje image. In: Advances in biometrics, ser. Lecture Notes in Computer science. Springer, vol 3832, pp 397–403
Kanematsu M, Takano H, Nakamura K (2007) Highly reliable liveness detection method for iris recognition. In: Annual conference SICE, pp 361–364
Jain AK, Bolle R, Pankanti S (1999) Personal identification in a networked society. Springer, New York, USA, pp 103–121
Galbally J, Ortiz-Lopez J, Fierrez J, Ortega-Garcia J (2012) Iris liveness detection based on quality related features. In: Proceedings of the 5th IAPR international conference on biometrics (ICB), pp 271–276
Mehrotra H et al (2012) Fast segmentation and adaptive SURF descriptor for iris recognition. Mathematical and computer modeling. Elsevier. https://doi.org/10.1016/j.mcm.2012.06.034
He Y et al (2012) Feature extraction of iris based on texture analysis. Advances in FCCS, vol 1, AISC 159. Springer, Berlin, Heidelberg, pp 541–546
Hu Y et al (2015) Iris liveness detection using regional features. Pattern Recogn Lett. https://doi.org/10.1016/j.patrec.2015.10.010
Sun Z, Tan T (2014) Iris Anti-spoofing. In: Marcel S et al (eds) Handbook of biometric anti-spoofing, advances in computer vision and pattern recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6524-8_6
Haralick RM, Shanmugam K, Dijnstein I (1973) Textural features for image classification.IEEE Transactions On Systems, Man, And Cybernetics, November 1973
Huang X, Ti C, Hou Q-Z, Tokuta A, Yang R (2013) An experimental study of pupil constriction for liveness detection. In: Proceeding of IEEE workshop applications of computer vision (WACV), pp 252–258
He X, Lu Y, Shi P (2009) A new fake iris detection method. In: Proceeding 3rd international conference on advanced biometrics, pp 1132–1139
Pacut A, Czajka A (2006) Aliveness detection for iris biometrics. In: Proceedings of 40th annual IEEE international carnahan conference security technology, pp 122–129
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Chavan, M.N., Patavardhan, P.P., Shinde, A.S. (2019). Scaled Conjugate Gradient Algorithm and SVM for Iris Liveness Detection. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_80
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DOI: https://doi.org/10.1007/978-3-030-00665-5_80
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