Abstract
Existing perspiration-based liveness detection algorithms need two successive images (captured in certain time interval), hence they are slow and not useful for real-time applications. Liveness detection methods using extra hardware increase the cost of the system. To alleviate these problems, we propose new curvelet-based method which needs only one fingerprint to detect liveness. Wavelets are very effective in representing objects with isolated point singularities, but fail to represent line and curve singularities. Curvelet transform allows representing singularities along curves in a more efficient way than the wavelets. Ridges oriented in different directions in a fingerprint image are curved; hence curvelets are very significant to characterize fingerprint texture. Textural measures based on curvelet energy and co-occurrence signatures are used to characterize fingerprint image. Dimensionalities of feature sets are reduced by running Pudil’s sequential forward floating selection (SFFS) algorithm. Curvelet energy and co-occurrence signatures are independently tested on three different classifiers: AdaBoost.M1, support vector machine and alternating decision tree. Finally, all the aforementioned classifiers are fused using the “Majority Voting Rule” to form an ensemble classifier. A fingerprint database consisting of 185 real, 90 Fun-Doh and 150 Gummy fingerprints is created by using varieties of artificial materials for casts and moulds of spoof fingerprints. Performance of the new liveness detection approach is found very promising, as it needs only one fingerprint and no extra hardware to detect vitality.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ratha N.K.: Enhancing security and privacy in biometrics-based authentication systems. IBM Syst. J. 40(3), 614–634 (2001)
Putte, T.V.D., Keuning, J.: Biometrical fingerprint recognition: don’t get your fingers burned. In: Proceedings of the Fourth Working Conf. Smart Card Res. Advanced Appl., pp. 289–303 (2000)
Matsumoto, T., Matsumoto, H., Yamada, K., Hoshino, S.: Impact of artificial ‘Gummy’ fingers on fingerprint systems. In: Proceedings of SPIE, vol. 4677, pp. 275–289 (2002)
Schuckers S.: Spoofing and anti-spoofing measures. Inf. Secur. Tech. Rep. 7(4), 56–62 (2002)
Maltoni D., Maio D., Jain A.K., Prabhakar S.: Handbook of Fingerprint Recognition. Springer, Berlin (2002)
Candes E.J., Donoho D.L.: Curvelets-a surprisingly effective nonadaptive representation for objects with edges. In: Cohen, A., Rabut, C., Schumaker, L.L. (eds) Curves and Surfaces, pp. 105–120. Vanderbilt University Press, Nashville (2000)
Starck J.L., Candes E.J., Donoho D.L.: The curvelet transform for image denoising. IEEE Trans. Image Process. 11(6), 670–684 (2002)
Do M.N., Vetterli M.: The finite Ridgelet transfrom for image reprsentation. IEEE Trans. Image Process. 12(1), 16–28 (2003)
Pudil P., Novovicova J., Kittler J.: Floating search methods in feature selection. Pattern Recognit. Lett. 15, 1119–1125 (1994)
Freund Y., Schapire R.E.: Decision-theoretic generalization of online learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
Vapnik V.N.: The Nature of Statistical Learning Theory (Information science and statistics). Springer, Berlin (1999)
LIBSVM—A Library for Support Vector Machine (SVM) Classifier. Available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Freund, Y., Mason, L.: The alternating decision tree algorithm. In: Proceedings of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, pp. 124–133. (1999)
Polikar, R.: Ensemble based systems in decision making-feature article. In: Proceedings of the IEEE Circuits and Systems Magazine, Third quarter, pp. 21–45 (2006)
Abhyankar, A., Schuckers, S.: Fingerprint liveness detection using local ridge frequencies and multiresolution texture analysis techniques. In: Proceedings of International Conference on Image Processing (ICIP), pp. 321–324 (2006)
Coli, P., Marcialis, G., Roli, F.: Power spectrum-based fingerprint vitality detection. In: Proceedings of the IEEE Workshop on Automatic Identification Advanced Technologies, pp. 169–173 (2007)
Parthasaradhi S., Derakhshani R., Hornak L., Schuckers S.: Time-series detection of perspiration as a liveness test in fingerprint scanners. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 35(3), 335–343 (2005)
Nikam, S.B., Agarwal, S.: Fingerprint liveness detection using curvelet energy and co-occurrence signatures. In: Proceedings of Fifth International Conference on Computer Graphics, Imaging and Visualisation (CGIV), pp. 217–222. (2008)
Details of plumber’s putty (M-seal adhesive) and gelatin (Fevi-gum). Available at http://www.pidilite.net
Details of dentist’s material (Aquasil soft putty of silicone and Zelgan 2002 powder). Available at http://www.dentsply-india.com
Details of dentist’s material (PYRAX-RR cold cure). Available at http://www.pyraxpolymers.com
Details of general purpose silicone sealant. Available at http://www.wacker.com
Fingerprint verification competition (FVC) 2004 database. Available at http://bias.csr.unibo.it/fvc2004/
Sklansky J.: Image segmentation and feature extraction. IEEE Trans. Syst. Man Cybern. 8, 237–247 (1978)
Hong M., Tong C.S.: A fast and effective model for wavelet subband histograms and its application in texture image retrieval. IEEE Trans. Image Process. 15(10), 3078–3088 (2006)
Haralick R.M., Shamugam K., Dinstein I.: Textural features for image classification. SMC 3(6), 610–621 (1973)
Details of WEKA data mining software. Available at http://www.cs.waikato.ac.nz/ml/weka/
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nikam, S.B., Agarwal, S. Curvelet-based fingerprint anti-spoofing. SIViP 4, 75–87 (2010). https://doi.org/10.1007/s11760-008-0098-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-008-0098-8