Abstract
This paper introduces a novel method based on the elasticity analysis of the finger skin to discriminate fake fingers from real ones. We match the fingerprints before and after special distortion and gained their corresponding minutiae pairs as landmarks. The thin-plate spline (TPS) model is used to globally describe the finger distortion. For an input finger, we compute the bending energy vector by the TPS model and calculate the similarity of the bending energy vector to the bending energy fuzzy feature set. The similarity score is in the range [0, 1], indicating how much the current finger is similar to the real finger. The method realizes fake finger detection based on the normal steps of fingerprint processing without special hardware, so it is easily implemented and efficient. The experimental results on a database of real and fake fingers show that the performance of the method is available.
Chapter PDF
References
Derakhshani, R., Schuckers, S.A.C., Hornak, L.A., O’Gorman, L.: Determination of vitality from a non-invasive biomedical measurement for use in fingerprint scanners. Pattern Recognition 36(2), 383–396 (2003)
Antonelli, A., Cappelli, R., Maio, D., Maltoni, D.: A new approach to fake finger detection based on skin distortion. In: Zhang, D., Jain, A.K. (eds.) Advances in Biometrics. LNCS, vol. 3832, pp. 221–228. Springer, Heidelberg (2005)
Hong, L., Wan, Y., Jain, A.K.: Fingerprint image enhancement: algorithms and performance evaluation. IEEE Trans. Pattern Analysis Machine Intelligence 20(8), 777–789 (1998)
Chen, X.J., Tian, J., Yang, X.: An algorithm for distorted fingerprint matching based on local triangle features set. IEEE Trans. on Information, Forensics and Security 1(2) (2006)
Bazen, A.M., Gerez, S.H.: Fingerprint matching by thin-plate spline modelling of elastic deformations. Pattern Recognition 36(8), 1859–1867 (2003)
Ross, A., Dass, S., Jain, A.K.: A deformable model for fingerprint matching. Pattern Recognition 38(1), 95–103 (2005)
Hoppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy cluster analysis: methods for classification, Data Analysis and Image Recognition. John Wiley & Sons, Chichester (1999)
Bookstein, F.L.: Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 2(6), 567–585 (1989)
Zhu, E., Yin, J.P., Zhang, G.M.: Fingerprint matching based on global alignment of multiple reference minutiae. Pattern Recognition 38(10), 1685–1694 (2005)
Matsumoto, T.H., Yamada, K., Hoshino, S.: Impact of Artificial ’Gummy’ Fingers on. Fingerprint Systems. In: Proceedings of SPIE, vol. 4677 (2002)
Rohr, K., Fornefett, M., Stiehl, H.S.: Approximating Thin-Plate Splines for Elastic Registration: Integration of Landmark Errors and Orientation Attributes. In: Proceedings of the 16th International Conference on Information Processing in Medical Imaging, pp. 252–265 (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, Y., Tian, J., Chen, X., Yang, X., Shi, P. (2007). Fake Finger Detection Based on Thin-Plate Spline Distortion Model. In: Lee, SW., Li, S.Z. (eds) Advances in Biometrics. ICB 2007. Lecture Notes in Computer Science, vol 4642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74549-5_78
Download citation
DOI: https://doi.org/10.1007/978-3-540-74549-5_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74548-8
Online ISBN: 978-3-540-74549-5
eBook Packages: Computer ScienceComputer Science (R0)