Abstract
Human iris being the most stable biometric modality suffers from presentation attacks like colored textured contact lenses and print attacks that obfuscate the natural iris texture. The paper presents discrete orthogonal moment-based invariant feature-set comprising of Tchebichef, Krawtchouk and Dual-Hahn moments which are extracted at localized iris regions to capture local intensity distributions of the iris texture. The orthogonal moment-based feature-set is made rotation, translation and scale-invariant in order to accommodate for geometric transformations when images are acquired in uncontrolled environment. The performance of the proposed techniques is evaluated using four publicly available iris spoofing databases: IIITD-Contact Lens Iris, IIITD Iris Spoofing, Clarkson LivDet 2015 and Warsaw LivDet 2015. The textured contact lens detection rate of 100% for IIITD-CLI and 99.48% for Clarkson datasets is achieved, respectively. Similarly, print+scan and print+capture attacks are detected with 99% and 98.93% accuracy for IIS datasets, respectively. The print attacks are detected with 99.63% and 98.89% accuracy for Clarkson and Warsaw datasets, respectively. The proposed techniques thus, prove to be effective in terms of contact lens and print attacks detection when acquired using multiple sensors.
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References
Arora SS, Vatsa M, Singh R, Jain A (2012) Iris recognition under alcohol influence: A preliminary study”, 5th IEEE IAPR International Conference on Biometrics (ICB), pp. 336–341
Daugman J (2003) Demodulation by complex-valued wavelets for stochastic pattern recognition. Int J Wavelets Multiresolution Inf Process 1(1):1–17
Daugman J (2004) How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 14(1):21–30
Doyle JS, Flynn PJ, Bowyer KW (2013) Automated classification of contact lens type in iris images. International Conference on Biometrics (ICB):1–6
Flom L, Safir A (1987) Iris recognition system. U.S. Patent 4 641 349
Flusser J, Suk T, Zitová B (2016) 3D Moment Invariants to translation, rotation, and scaling. 2D and 3D Image Analysis by Moments 96
Galbally J, Marcel S, Fierrez J, J. (2014) Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition. IEEE Trans Image Process 23(2):710–724
Gan J, Liang Y (2005) Applications of wavelet packets decomposition in iris recognition. Advances in Biometrics. Berlin, Germany: Springer-Verlag:443–449
Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2015) An investigation of local descriptors for biometric spoofing detection. IEEE Transactions on Information Forensics and Security 10(4):849–863
Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2015) Local contrast phase descriptor for fingerprint liveness detection. Pattern Recogn 48(4):1050–1058
Gupta P, Behera S, Vatsa M, Singh R (2014) On iris spoofing using print attack. 22nd IEEE International Conference on Pattern Recognition:1681–1686
Han M, Pei J (2011) Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers, San Francisco
He X, An S, Shi P (2007) Statistical texture analysis-based approach for fake Iris detection using support vector machines. Proc IAPR Int Conf Biometrics:540–546
Hollingsworth K, Bowyer KW, Flynn PJ (2009) Pupil dilation degrades iris biometric performance. Comput Vis Image Underst 113(1):150–157
Hu MK (1962) Visual Pattern Recognition by Moment Invariants. IRE Transactions on Information Theory 8(2):179–187
Hu Y, Sirlantzis K, Howells G (2016) Iris liveness detection using regional features. Pattern Recogn Lett 82(2):242–250
Jain AK, Ross AA, Nandakumar K (2011) Introduction to biometrics. Springer Science & Business Media, Berlin
Kaur B (2019) Discrete Orthogonal Moments for iris recognition https://www.mathworks.com/matlabcentral/fileexchange/72412-discrete-orthogonal-moments-for-iris-recognition. MATLAB Central File Exchange. Retrieved August 15, 2019
Kaur B (2019). Dual-Hahn mOMENTS. https://www.mathworks.com/matlabcentral/fileexchange/72413-dual-hahn-moments. MATLAB Central File Exchange. Retrieved August 15, 2019
Kaur B, Joshi G (2016) Lower Order Krawtchouk Moment-Based Feature-Set for Hand Gesture Recognition. Advances in Human-Computer Interaction, Hindawi Publications 2016(2016):1–10
Kaur B, Joshi G, Vig R (2015) Analysis of shape recognition capability of Krawtchouk moments. IEEE International Conference on Computing, Communication and Automation:1085–1090
Kaur B, Joshi G, Vig R (2017) Indian sign language recognition using Krawtchouk moment-based local features. The Imaging Science Journal 65(3):171–179
Kaur B, Joshi G, Vig R (2017) Identification of ISL alphabets using discrete orthogonal moments. Wirel Pers Commun 95(4):4823–4845
Kaur B, Singh S, Kumar J (2018) Robust Iris Recognition using Moment Invariants. Wirel Pers Commun 99(2):799–828
Kaur B, Singh S, Kumar J (2018) A Study on Fake Iris Detection under Spoofing Attacks. J Eng Appl Sci 13(8):2049–2056
Kaur B, Singh S, Kumar J (2018) Fusing Iris and Periocular Recognition using Discrete Orthogonal Moment-based Invariant Feature-set. International Journal of Biometrics 10(4):352–367
Kaur B, Singh S, Kumar J (2018) Iris Recognition using Zernike Moments and Polar Harmonic Transforms. Arab J Sci Eng 43(12):7209–7218
Kaur B, Singh S, Kumar J (2019) Cross- Sensor Iris Spoofing Detection using Orthogonal Features. Comput Electr Eng 73:279–288
Kaur B, Singh S, Kumar J (2019) Orthogonal rotation invariant features for iris and periocular recognition. International Journal of Biometrics 11(2):160–176
Kohli N, Yadav D, Vatsa M, Singh R (2013) Revisiting Iris Recognition with Color Cosmetic Contact Lenses. Proc. 6th IAPR, pp. 1–5
Kohli N, Yadav D, Vatsa M, Singh R, Noore A (2016) Detecting medley of iris spoofing attacks using DESIST. IEEE 8th International Conference on Biometrics Theory, Applications and Systems:1–6
Ma L, Tan T, Wang Y, Zhang D (2003) Personal identification based on iris texture analysis. IEEE Trans Pattern Anal Mach Intell 25(12):1519–1533
Masek L (2003) Recognition of human iris patterns for biometric identification, pp. 1–7. http://www.csse.uwa.edu.au/opk/student projects/labor
Menotti D, Chiachia G, Pinto A, Schwartz WR, Pedrini H, Falcao AX, Rocha A (2015) Deep representations for iris, face, and fingerprint spoofing detection. IEEE Transactions on Information Forensics and Security 10(4):864–879
Mukundan R, Lee PA (2001) Image Analysis by Tchebichef Moments. IEEE Trans Image Process 10(9):1357–1364
Nalla PR, Kumar A (2017) Toward More Accurate Iris Recognition Using Cross-Spectral Matching. IEEE Trans Image Process 26(1):208–221
Nichols JJ (2012) Annual Report: Contact Lenses. Available:http://www.clspectrum.com/articleviewer.aspx?articleID=107853, accessed 26 June 2017
Pala F, Bhanu B (2017) Iris Liveness Detection by Relative Distance Comparisons. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops:162–169
Pillai JK, Puertas M, Chellappa R (2014) Cross-sensor iris recognition through kernel learning. IEEE Trans Pattern Anal Mach Intell 36(1):73–85
Priyal SP, Bora PK (2013) A Robust Static Hand Gesture Recognition System using Geometry based Normalizations and Krawtchouk Moments. Pattern Recogn Lett 46(8):2202–2219
Raghavendra R, Raja KB, Busch C (2014) Ensemble of statistically independent filters for robust contact lens detection in iris images. ACM Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing, pp. 24:1–24:7
Raghavendra R, Raja KB, Busch C (2017) ContlensNet: Robust Iris Contact Lens Detection Using Deep Convolutional Neural Networks. IEEE Winter Conference on Applications of Computer Vision:1160–1167
Rahman SM, Howlader T, Hatzinakos D (2016) On the selection of 2D Krawtchouk moments for face recognition. Pattern Recogn 54:83–93
Sequeira AF, Murari J, Cardoso JS (2014) Iris liveness detection methods in the mobile biometrics scenario. International Joint Conference on Neural Networks (IJCNN):3002–3008
Silva P, Luz E, Baeta R, Pedrini H, Falcao AX, Menotti D (2015) An approach to iris contact lens detection based on deep image representations. 28th IEEEE SIBGRAPI Conference on Graphics, Patterns and Images:157–164
Tan CW, Kumar A (2013) Adaptive and localized iris weight map for accurate iris recognition under less constrained environments. Proc IEEE 6th Int Conf Biometrics, Theory Appl Syst (BTAS):1–7
Teague MR (1980) Image Analysis via the General Theory of Moments. J Opt Soc Am 70(8):920–930
Tsougenis ED, Papakostas GA, Koulouriotis DE, Tourassis VD (2012) Performance Evaluation of Moment-based Watermarking Methods: A Review. J Syst Softw 85(8):1864–1884
UID Authority of India (2012) Role of biometric technology in Aadhaar enrollments. Available : http://uidai.gov.in/images/FrontPageUpdates/role_of_biometric_technology_in_aadhaar_jan21_2012.pdf. accessed 26 June, 2017
Wei Z, Qiu X, Sun Z, Tan T (2008) Counterfeit Iris detection based on texture analysis. Proc 18th Int Conf Pattern Recognit:1–4
Yadav D, Kohli N, Doyle J, Singh R, Vatsa M, Bowyer K (2014) Unraveling the Effect of Textured Contact Lenses on Iris Recognition. IEEE Transaction on Information Forensics and Security 9(5):851–862
Yambay D, Walczak B, Schuckers S, Czajka A (2017) LivDet-Iris 2015 - Iris Liveness Detection. IEEE International Conference on Identity, Security and Behavior Analysis:1–6
Yap PT, Raveendran P, Ong SH (2003) Image analysis by Krawtchouk moments. IEEE Trans Image Process 12(11):1367–1377
Yap PT, Raveendran P, Ong SH (2007) Image analysis using Hahn moments. IEEE Trans Pattern Anal Mach Intell 29(11):2057–2062
Zhang H, Sun Z, Tan T (2010) Contact lens detection based on weighted LBP. Proc 20th Int Conf Pattern Recognit:4279–4282
Acknowledgments
The authors would like to acknowledge Indraprastha Institute of Information and Technology, Delhi (IIITD), Clarkson University, USA and Warsaw University of Technology, Poland for providing IIITD-CLI, IIS, Clarkson and Warsaw iris spoofing databases used in this work.
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Kaur, B. Iris spoofing detection using discrete orthogonal moments. Multimed Tools Appl 79, 6623–6647 (2020). https://doi.org/10.1007/s11042-019-08281-x
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DOI: https://doi.org/10.1007/s11042-019-08281-x