Performances of biometric recognition systems can degrade quickly when the input biometric traits exhibit substantial variations compared to the templates collected during the enrollment stage of the system’s users. On the other hand, a lot of new unlabelled biometric data, which could be exploited to adapt the system to input data variations, are made available during the system operation over the time. This chapter deals with adaptive biometric systems that can improve with use by exploiting unlabelled data. After a critical review of previous works on adaptive biometric systems, the use of semisupervised learning methods for the development of adaptive biometric systems is discussed. Two examples of adaptive biometric recognition systems based on semisupervised learning are presented in the chapter, and the concept of biometric co-training is introduced for the first time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Balcan, M.F., Blum, A., Choi, P.P., Lafferty, J., Pantano, B., Rwebangira, M.R., and Zhu, X. (2005) Person identification in webcam images: An application of semi-supervised learning, ICML2005 Workshop on Learning with Partially Classified Training Data, Bonn, Germany, 7 August.
Blum, A., Mitchell, T. (1998) Combining labeled and unlabeled data with co-training, Proc. of the Workshop on Computational Learning Theory, pp. 92-100.
Castelli V., Cover, T.M. (1995) On the exponential value of labeled samples, Pattern Recognition Letters, 16: 105-111.
Cohen, I., Cozman, F.G., Sebe, N., Cirelo, M.C., and Huang, T. (2004) Semi-supervised learning of classifiers: theory, algorithms and their applications to human-computer interaction, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(12): 1553-1567.
Du, W., Kohei, I., Kiichi, U., Lipo, W., and Yaochu, J. (2005) Dimensionality reduction for semi-supervised face recognition, Proc. International Conference on Fuzzy Systems and Knowledge Discovery, LNCS 3614, Springer Verlag, New York, pp. 1314-1334.
Gauvain, J.L., Lee, C.H. (1994) Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. IEEE Transactions on Speech and Audio Processing, 2(2): 291-298.
Hand, D. J. (2006) Classifier technology and the illusion of progress. Statistical Science, 21(1): 1-15.
Jain, A.K., Hong, L., and Bolle, R., 1997. On-line fingerprint verification. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(4): 302-314.
Jain, A.K., Pankanti, S., Prabhakar, S., Hong, L., Ross, A., and Wayman, J.L. (2004) Biometrics: A grand challenge, Proc. International Conference on Pattern Recognition (ICPR), (Cambridge, UK), Vol. 2, pp. 935-942.
Jiang, X., Ser, W. (2002) Online fingerprint template improvement, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(8, August): 1121-1126.
Kelly, M.G., Hand, D.J., and Adams, N..M. (1999) The impact of changing populations on classifier performance. In Proc. 5th ACM SIGDD Interna-tional Conference on Knowledge Discovery and Data Mining, San Diego, CA, ACM Press, New York, pp. 367-371.
Kemp, T., Waibel, A. (1999) Unsupervised training of a speech recognizer: Recent experiments, Proc. Eurospeech, Vol. 6, pp. 2725-2728.
Lijin, A. (2002) Recognizing and remembering individuals: Online and unsu-pervised face recognition for humanoid robot, Proc. 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2002), Vol. 2, pp. 1202-1207.
Liu, X., Chen, T., and Thornton, S.M. (2003) Eigenspace updating for nonstationary process and its application to face recognition. Pattern Recognition, pp. 1945-1959.
Maio, D., Maltoni, D., Cappelli, R., Wayman J.L., and Jain, A.K. (2002) FVC2002: Second Fingerprint Verification Competition, Proc. 16th Inter-national Conference on Pattern Recognition (ICPR2002), Québec City, Vol. 3, pp. 811-814.
Marcialis, G.L., Roli, F. (2004) Fingerprint verification by fusion of optical and capacitive sensors, Pattern Recognition Letters, 25(11): 1315-1322.
Martinez, A., Benavente, R. (1998) The AR face database. CVC Technical Report #24, June.
Martinez, C., Fuentes, O. (2003) Face recognition using unlabeled data, Computacion y Sistems, Iberoamerican Journal of Computer Science Research, 7(2): 123-129.
Melville, P., Mooney, R. (2004) Diverse ensembles for active learning, 21st International Conference on Machine Learning, Article no. 74, Canada.
Nagy, G., (2004a) Classifiers that improve with use, Proc. Conference on Pattern Recognition and Multimedia, IEICE Pub. Vol. 103 No. 658, Tokyo, pp. 79-86.
Nagy, G. (2004b) Visual pattern recognition in the years ahead, Proc. International Conference on Pattern Recognition XVII, Vol. IV, Cambridge, UK, August, pp. 7-10.
Nagy, G. (2005) Interactive, mobile, distributed pattern recognition. Proc. International Conference on Image Analysis and Processing (ICIAP05), LNCS 3617, Springer, New York, pp. 37-49.
Nigam, K., McCallum, A.K., Thrun, S., and Mitchell, T. (2000) Text classification from labeled and unlabeled documents using EM, Machine Learning, 39: 103-134.
Okada, K., Lawrence Kite, L., and von der Malsburg, C. An adaptive person recognition system. (2001), Proc. IEEE Int. Workshop on Robot-Human Interactive Communication, pp. 436-441.
Okada, K., von der Malsburg, C. (1999) Automatic video indexing with incre-mental gallery creation: Integration of recognition and knowledge acquisi-tion, Proc. ATR Symposium on Face and Object Recognition, pp. 153-154, Kyoto, July 19-23.
Rhodes, K.A. (2004) Aviation Security, Challenges in Using Biometric Technologies. USA General Accounting Office.
Roli, F. (2005) Semi-supervised multiple classifier systems: Background and research directions. 6th International Workshop on Multiple Classifier Sys-tems (MCS 2005), Seaside, CA, USA, June 13-15, N.C. Oza, R. Polikar, J. Kittler, and F. Roli (Eds.), LNCS 3541, Springer-Verlag, New York, pp. 1-11.
Roli, F., Marcialis, G.L. (2006) Semi-supervised PCA-based face recognition using self-training, Joint IAPR Int. Workshop. on Structural and Syntacti-cal Pattern Recognition and Statistical Techniques in Pattern Recognition, August, 17-19, Hong Kong (China), D. Yeung, J. Kwok, A. Fred, F. Roli, and D. de Ridder (Eds.), LNCS 4109, Springer, New York, pp. 560-568.
Ross, A., Nandakumar, K., and Jain, A.K. (2006) Handbook of Multibiometrics, Springer, New York.
Ryu, C., Hakil, K., and Jain, A.K. (2006) Template adaptation based fingerprint verification. Proc. International Conference on Pattern Recognition (ICPR), Vol. 4, pp. 582-585, Hong Kong, August.
Seeger, M. (2002) Learning with labeled and unlabeled data, Technical Report, University of Edinburgh, Institute for Adaptive and Neural Computation, pp. 1-62.
Sinha, P., Balas, B.J., Ostrovsky, Y., and Russell, R. (2006a) Face recognition by humans: Nineteen results all computer vision researchers should know about. Proceedings of the IEEE, 94(11):1948-1962.
Sinha, P., Balas, B.J., Ostrovsky, Y., and Russell, R. (2006b) Face recognition by humans. in Face Processing: Advanced Modeling & Methods. Zhao, W. and Chellappa, R. (Eds.), Academic Press, pp. 257-292.
Sukthankar, R., Stockton, R. (2001) Argus: The digital doorman, Intelligent Systems, IEEE (See also IEEE Intelligent Systems and Their Applications), (March/April) 16(2):14-19.
Tan, X., Chen, S., Zhou, Z.-H., and Zhang, F. (2006) Face recognition from a single image per person: a survey. Pattern Recognition, 39(9): 1725-1745.
Tur, G., Hakkani-Tur D., and Schapire, R.E. (2005) Combining active and semi-supervised learning for spoken language understanding. Speech Communication, 45: 171-186.
Turk, M., Pentland, A. (1991) Eigenfaces for face recognition, Journal of Cognitive Neuroscience, 3(1): 71-86.
Uludag, U., Ross, A., and Jain, A. K. (2004) Biometric template selection and update: A case study in fingerprints, Pattern Recognition, 37(7, July): 1533-1542.
Weng, J.J. and Hwang, W.-S. (1998) Toward automation of learning: The state self-organization problem for a face recognizer, Proc. Third IEEE Int. Conference on Automatic Face and Gesture Recognition, pp. 384-389.
Zhu, X. (2006) Semi-supervised learning literature survey, Technical report, Computer Sciences TR 1530, University of Wisconsin, Madison, USA.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag London Limited
About this chapter
Cite this chapter
Roli, F., Didaci, L., Marcialis, G.L. (2008). Adaptive Biometric Systems That Can Improve with Use. In: Ratha, N.K., Govindaraju, V. (eds) Advances in Biometrics. Springer, London. https://doi.org/10.1007/978-1-84628-921-7_23
Download citation
DOI: https://doi.org/10.1007/978-1-84628-921-7_23
Publisher Name: Springer, London
Print ISBN: 978-1-84628-920-0
Online ISBN: 978-1-84628-921-7
eBook Packages: Computer ScienceComputer Science (R0)