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Introduction to Adaptive Biometric Systems

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Adaptive Biometric Systems

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Biometric person recognition poses a very challenging pattern recognition problem because of large variability in biometric sample quality encountered during testing and a restricted number of enrollment samples for training. Furthermore, biometric traits can change over time due to aging and change of lifestyle. Effectively, the noise factors encountered in testing cannot be represented by the limited training samples . A promising solution to training data deficiency and ageing is to use an adaptive biometric system. These systems attempt to adapt themselves to follow the change in the input biometric data. Adaptive biometrics deserves a treatment on its own right because standard machine-learning algorithms cannot readily handle changing signal quality. The aim of this chapter is to introduce the concept of adaptive biometric systems in terms of taxonomy, level of adaptation, open issues and challenges involved.

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Notes

  1. 1.

    A template refers to the biometric sample used for enrollment. The term “model” refers to statistical representation derived from one or more biometric samples. In order for our discussion to cover both types of methods, we shall adapt the standard vocabulary, that is, “biometric reference” or simply reference. A reference is subsequently used for comparing a biometric test/query sample to obtain a similarity score.

References

  1. Akhtar, Z., Ahmed, A., Erdem, C.E., Foresti, G.L.: Biometric template update under facial aging. In: Proceedings of the IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (IEEE SSCI-CIBIM) (2014)

    Google Scholar 

  2. Poh, N., Kittler, J., Rattani, A., Tistarelli, M.: Group-specific score normalization for biometric systems. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 38–45 (2010)

    Google Scholar 

  3. Rattani, A., Marcialis, G.L., Roli, F.: Biometric system adaptation by self-update and graph-based techniques. J. Vis. Lang. Comput. 24(1), 1–9 (2013)

    Article  Google Scholar 

  4. Rattani, A., Marcialis, G.L., Granger, E., Roli, F.: A dual-staged classification-selection approach for automated update of biometric templates. In: International Conference on Pattern Recognition (ICPR), pp. 2972–2975 (2014)

    Google Scholar 

  5. Tsymbal, A.: The problem of concept drift: definitions and related work. Department of Computer Science, Trinity College, Ireland (2004)

    Google Scholar 

  6. Rattani, A.: Adaptive biometric system based on template update procedures, Ph.D. thesis, University of Cagliari, Italy (2010)

    Google Scholar 

  7. Akhtar, Z., Rattani, A., Hadid, A., Tistarelli, M.: Face recognition under ageing effect: a comparative analysis. In: Proceedings of the International Conference on Image Analysis and Processing (ICIAP), pp. 309–318 (2013)

    Google Scholar 

  8. Ling, H., Soatto, S., Ramanathan, N., Jacobs, D.W.: A study of face recognition as people age. In: Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV), pp. 1–8 (2007)

    Google Scholar 

  9. Ramanathan, N., Chellappa, R.: Face verification across age progression. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition (CVPR), pp. 462–469 (2005)

    Google Scholar 

  10. Lanitis, A., Taylor, C.J., Cootes, T.F.: Toward automatic simulation of aging effects on face images. IEEE Tran. Pattern Anal. Mach. Intell. 24(4), 442–455 (2002)

    Google Scholar 

  11. Uludag, U., Ross, A., Jain, A.: Biometric template selection and update: a case study in fingerprints. Pattern Recognit. 37(7), 1533–1542 (2004)

    Article  MATH  Google Scholar 

  12. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Article  MATH  Google Scholar 

  13. Rattani, A., Marcialis, G.L., Roli, F.: Temporal analysis of biometric template update procedures in uncontrolled environment. In: Proceedings of 16th International Conference on Image Analysis and Processing (ICIAP’11), Raveena, Italy, pp. 595–604 (2011)

    Google Scholar 

  14. Poh, N., Rattani, A., Roli, F.: Critical analysis of adaptive biometric systems. IET Biom. 1(4), 179–187 (2012)

    Article  Google Scholar 

  15. Rattani, A., Freni, B., Marcialis, G.L., Roli, F.: Template update methods in adaptive biometric systems: a critical review. In: Proceedings of the International Conference on Biometrics (ICB), pp. 847–857 (2009)

    Google Scholar 

  16. Pavani, S.K., Sukno, F.M., Butakoff, C., Planes, X., Frangi, A.F.: A confidence based update rule for self-updating human face recognition systems. In: Proceedings of the International Conference on Biometrics (ICB), pp. 151–160 (2009)

    Google Scholar 

  17. Rattani, A., Marcialis, G.L., Roli, F.: Biometric template update using the graph mincut: a case study in face verification. In: Proceedings of the 6th IEEE Biometric Symposium (2008)

    Google Scholar 

  18. Liu, X., Chen, T., Thornton, S.M.: Eigenspace updating for non-stationary process and its application to face recognition. Pattern Recognit. 1945–1959 (2003)

    Google Scholar 

  19. Franco, A., Maio, D., Maltoni, D.: Incremental template updating for face recognition in home environments. Pattern Recognit. 43, 2891–2903 (2010)

    Article  MATH  Google Scholar 

  20. Jiang, X., Ser, W.: Online fingerprint template improvement. IEEE Tran. PAMI 8, 1121–1126 (2002)

    Article  Google Scholar 

  21. Roli, F., Marcialis, G.L.: Semi-supervised PCA-based face recognition using self training. In: Proceedings of the Joint ICPR International Workshop on S+SSPR (2006)

    Google Scholar 

  22. Poh, N., Kittler, J., Marcel, S., Matrouf, D., Bonastre, J.F.: Model and score adaptation for biometric systems: coping with device interoperability and changing acquisition conditions. In: Proceedings of the 20th International Conference on Pattern Recognition (ICPR), pp. 1229–1232 (2010)

    Google Scholar 

  23. Ryu, C., Hakil, K., Jain, A.: Template adaptation based fingerprint verification. In: Proceedings of the International Conference on Pattern Recognition (ICPR), pp. 582–585 (2006)

    Google Scholar 

  24. Rattani, A., Marcialis, G.L., Roli, F.: An experimental analysis of the relationship between biometric template update and the doddingtons zoo in face verification. In: Proceedings of 14th International Conference on Image Analysis and Processing (ICIAP’09), Vietri sul Mare (Italy), pp. 434–442 (2009)

    Google Scholar 

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Authors and Affiliations

  1. Michigan State University, East Lansing, USA

    Ajita Rattani

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  1. Ajita Rattani
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Correspondence to Ajita Rattani .

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Editors and Affiliations

  1. Michigan State University, East Lansing, Michigan, USA

    Ajita Rattani

  2. University of Cagliari, Cagliari, Italy

    Fabio Roli

  3. ETS, Montréal, Québec, Canada

    Eric Granger

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Rattani, A. (2015). Introduction to Adaptive Biometric Systems. In: Rattani, A., Roli, F., Granger, E. (eds) Adaptive Biometric Systems. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-24865-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-24865-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24863-9

  • Online ISBN: 978-3-319-24865-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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