Abstract
This paper describes an empirical study to investigate the performance of a wide range of classifiers deployed in applications to classify biometric data. The study specifically reports results based on two different modalities, the handwritten signature and fingerprint recognition. We demonstrate quantitatively how performance is related to classifier type, and also provide a finer-grained analysis to relate performance to specific non-biometric factors in population demographics. The paper discusses the implications for individual modalities, for multiclassifier but single modality systems, and for full multibiometric solutions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Nist Fingerprint Image 2. User’s guide to
Allah, M.M.A.: Artificial neural networks based fingerprint authentication with clusters algorithm. Informatica (Slovenia) 29(3), 303–308 (2005)
Allah, M.M.A.: A novel line pattern algorithm for embedded fingerprint authentication system. ICGST International Journal on Graphics, Vision and Image Processing 5, 29–35 (2005)
Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM 45(6), 891–923 (1998)
Buhmann, M.D.: Radial Basis Functions. Cambridge University Press, New York (2003)
Canuto, A.M.P.: Combining Neural Networks and Fuzzy Logic for Aplications in Character Recognition. PhD thesis, Department of Electronics, University of Kent, Canteburry, UK, Maio (2001)
Chen, Y., Dass, S.C., Jain, A.K.: Fingerprint quality indices for predicting authentication performance. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 160–170. Springer, Heidelberg (2005)
Chikkerur, S., Cartwright, A.N., Govindaraju, V.: Fingerprint enhancement using stft analysis. Pattern Recognition Letter 40(1), 198–211 (2007)
Elkan, C.: Boosting and naive bayesian learning. Technical report (1997)
Fürnkranz, J., Widmer, G.: Incremental reduced error pruning. In: ICML, pp. 70–77 (1994)
Guest, R.M.: The repeatability of signatures. In: IWFHR 2004: Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR 2004), Washington, DC, USA, pp. 492–497. IEEE Computer Society, Los Alamitos (2004)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River (1998)
Khan, N.Y., Javed, M.Y., Khattak, N., Chang, U.M.Y.: Optimization of core point detection in fingerprints. In: DICTA, pp. 260–266 (2007)
Leisch, F., Jain, L.C., Hornik, K.: Cross-validation with active pattern selection for neural-network classifiers. IEEE Transactions on Neural Networks 9(1), 35–41 (1998)
Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: Fvc2002: Second fingerprint verification competition. In: ICPR 2002: Proceedings of the 16 th International Conference on Pattern Recognition (ICPR 2002), Washington, DC, USA, vol. 3, p. 30811. IEEE Computer Society, Los Alamitos (2002)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Nello, C., John, S.-T.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (March 2000)
Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Rosenblatt, F.: The perception: a probabilistic model for information storage and organization in the brain, pp. 89–114 (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Abreu, M., Fairhurst, M. (2008). An Empirical Comparison of Individual Machine Learning Techniques in Signature and Fingerprint Classification. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds) Biometrics and Identity Management. BioID 2008. Lecture Notes in Computer Science, vol 5372. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89991-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-89991-4_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89990-7
Online ISBN: 978-3-540-89991-4
eBook Packages: Computer ScienceComputer Science (R0)