Abstract
Biometric systems compare biometric samples to produce matching scores. However, the corresponding distributions are often heterogeneous and as a result it is hard to specify a threshold that works well in all cases. Score normalization techniques exploit the score distributions to improve the recognition performance. The goals of this chapter are to (i) introduce the reader to the concept of score normalization and (ii) answer important questions such as why normalizing matching scores is an effective and efficient way of exploiting score distributions, and when such methods are expected to work. In particular, the first section highlights the importance of normalizing matching scores; offers intuitive examples to demonstrate how variations between different (i) biometric samples, (ii) modalities , and (iii) subjects degrade recognition performance ; and answers the question of why score normalization effectively utilizes score distributions. The next three sections offer a review of score normalization methods developed to address each type of variation. The chapter concludes with a discussion of why such methods have not gained popularity in the research community and answers the question of when and how one should use score normalization.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akhtar, Z., Fumera, G., Marcialis, G., Roli, F.: Evaluation of serial and parallel multibiometric systems under spoofing attacks. In: Proceedings of 5th International Conference on Biometrics: Theory, Applications and Systems, pp. 283–288, New Delhi, India, March 29–April1 2012
Auckenthaler, R., Carey, M., Lloyd-Thomas, H.: Score normalization for text-independent speaker verification systems. Digit. Signal Proc. 10(1), 42–54 (2000)
Beveridge, J., Phillips, P., Bolme, D., Draper, B., Givens, G., Lui, Y., Teli, M., Zhang, H., Scruggs, W., Bowyer, K., Flynn, P., Cheng, S.: The challenge of face recognition from digital point-and-shoot cameras. In Proceedings of 6th International Conference on Biometrics: Theory, Applications and Systems, pp. 1–8, Washington DC, 4–7 June 2013
Birnbaum, M., Patton, J., Lott, M.: Evidence against rank-dependent utility theories: tests of cumulative independence, interval independence, stochastic dominance, and transitivity. Organ. Behav. Hum. Decis. Process. 77(1), 44–83 (1999)
Doddington, G., Liggett, W., Martin, A., Przybocki, M., Reynolds, D.: Sheep, goats, lambs and wolves: A statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation. In Proceedings of the International Conference on Spoken Language Processing, vol. 4, pp. 1–4, Sydney, Australia, Nov 30–Dec 4 1998
Durlauf, S., Blume, L.: The New Palgrave Dictionary of Economics. Palgrave Macmillan, Basingstoke (2008)
Fortuna, J., Sivakumaran, P., Ariyaeeinia, A., Malegaonkar, A.: Relative effectiveness of score normalisation methods in open-set speaker identification. In: Proceedings of the Speaker and Language Recognition Workshop, Toledo, Spain, May 31 June 3 2004
Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recogn. 38(12), 2270–2285 (2005)
Jain, A., Ross, A.: Multibiometric systems. Commun. ACM 47(1), 34–40 (2004)
Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)
Liu, Y., Yang, L., Suen, C.: The effect of correlation and performances of base-experts on score fusion. Trans. Syst. Man Cybern. Syst. 44(4), 510–517 (2014)
Makihara, Y., Muramatsu, D., Iwama, H., Ngo, T., Yagi, Y.: Score-level fusion by generalized Delaunay triangulation. In: Proceedings of 2nd International Joint Conference on Biometrics, pp. 1–8, Clearwater, FL, Sep 29 Oct 2 2014
Makihara, Y., Muramatsu, D., Yagi, Y., Hossain, A.: Score-level fusion based on the direct estimation of the bayes error gradient distribution. In: Proceedings of the International Joint Conference on Biometrics, pp. 1–8, Washington DC, 11–13 Oct 2011
Mezai, L., Hachouf, F., Bengherabi, M.: Score fusion of face and voice using DempsterShafer theory for person authentication. In: Proceedings of 11th International Conference on Intelligent Systems Design and Applications, pp. 894–899. Cordoba, Spain, 22–24 Nov 2011
Moutafis, P., Kakadiaris, I.: Can we do better in unimodal biometric systems? A novel rank-based score normalization framework for multi-sample galleries. In: Proceedings of 6th IARP International Conference on Biometrics, Madrid, Spain, 4–7 June 2013
Moutafis, P., Kakadiaris, I.: Can we do better in unimodal biometric systems? A novel rank-based score normalization framework. Trans. Cybern. 99, 1–14 (2014, In Press)
Moutafis, P., Kakadiaris, I.: Rank-based score normalization for multi-biometric score fusion. In: Proceedings of 8th International Symposium on Technologies for Homeland Security, Waltham, MA, 14–15 April 2015
Nguyen, K., Denman, S., Sridharan, S., Fookes, C.: Score-level multibiometric fusion based on Dempster-Shafer theory incorporating uncertainty factors. Trans. Hum. Mach. Syst. 99, 1–9 (2014)
Ocegueda, O., Passalis, G., Theoharis, T., Shah, S., Kakadiaris, I.: UR3D-C: linear dimensionality reduction for efficient 3D face recognition. In: Proceedings of the International Joint Conference on Biometrics, pp. 1–6, Washington DC, Oct 11–13 2011
Pittsburgh Pattern Recognition.: PittPatt face recognition software development kit (PittPatt SDK) v5.2, March 2011
Poh, N., Bengio, S.: An investigation of f-ratio client-dependent normalisation on biometric authentication tasks. Technical report, 04-46, IDIAP, Martigny, Switzerland (2004)
Poh, N., Bengio, S.: How do correlation and variance of base-experts affect fusion in biometric authentication tasks? Trans. Signal Process. 53(11), 4384–4396 (2005)
Poh, N., Kittler, J.: Incorporating variation of model-specific score distribution in speaker verification systems. IEEE Trans. Audio Speech Lang. Process. 16(3), 594–606 (2008)
Poh, N., Kittler, J.: A methodology for separating sheep from goats for controlled enrollment and multimodal fusion. In: Proceedings of 6th Biometrics Symposium, pp. 17–22, Tampa, FL, 23–25 Sept 2008
Poh, N., Kittler, J.: A biometric menagerie index for characterising template/model specific variation. In: Proceedings of 3rd International Conference on Biometrics, pp. 1–10, Sassari, Italy, 2–9 June 2009
Poh, N., Kittler, J., Alkoot, F.: A discriminative parametric approach to video-based score-level fusion for biometric authentication. In: Proceedings of 21st International Conference on Pattern Recognition, vol. 3, pp. 2335–2338 (2012)
Poh, N., Kittler, J., Bourlai, T.: Improving biometric device interoperability by likelihood ratio-based quality dependent score normalization. In: Proceedings of 1st International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–5, Washington DC, 27–29 Sept 2007
Poh, N., Kittler, J., Bourlai, T.: Quality-based score normalization with device qualitative information for multimodal biometric fusion. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 40(3), 539–554 (2010)
Poh, N., Kittler, J., Rattani, A., Tistarelli, M.: Group-specific score normalization for biometric systems. In: Proceedings of 23rd IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 38–45, San Francisco, CA, 13–18 June 2010
Poh, N., Merati, A., Kittler, J.: Heterogeneous information fusion: a novel fusion paradigm for biometric systems. In: Proceedings of the International Joint Conference on Biometrics, pp. 1–8, Washington, DC, 10–13 Oct 2011
Poh, N., Tistarelli, M.: Customizing biometric authentication systems via discriminative score calibration. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, Providence, RI, June 16–21 2012
Poh, N., Tistarelli, M.: Customizing biometric authentication systems via discriminative score calibration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2681–2686, Providence, RI, 16–21 June 2012
Rua, E., Castro, J.L., Mateo, C.: Quality-based score normalization for audiovisual person authentication. In: Proceedings of the International Conference on Image Analysis and Recognition, pp. 1003–1012, Povoa de Varzim, Portugal, 25–27 June 2008
Scheirer, W., Kumar, N.: Multi-attribute spaces: calibration for attribute fusion and similarity search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2933–2940, Providence, RI, 16–21 June 2012
Scheirer, W., Rocha, A., Michaels, R., Boult, T.: Meta-recognition: the theory and practice of recognition score analysis. Trans. Pattern Anal. Mach. Intell. 33, 1689–1695 (2011)
Scheirer, W., Rocha, A., Micheals, R., Boult, T.: Robust fusion: extreme value theory for recognition score normalization. In: Proceedings of the European Conference on Computer Vision, vol. 6313, pp. 481–495. Crete, Greece, 5–11 Sept 2010
Sun, Z., Tan, T.: CASIA iris image database, 14 Aug 2014 (2012)
Toderici, G., Evangelopoulos, G., Fang, T., Theoharis, T., Kakadiaris, I.: UHDB11 database for 3D-2D face recognition. In: Proceedings of the Pacific-Rim Symposium on Image and Video Technology, pp. 1–14, 28 Oct 2013
Toderici, G., Passalis, G., Zafeiriou, S., Tzimiropoulos, G., Petrou, M., Theoharis, T., Kakadiaris, I.: Bidirectional relighting for 3D-aided 2D face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2721–2728, San Francisco, CA, 13–18 June 2010
Tyagi, V., Ratha, N.: Biometric score fusion through discriminative training. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 145–149, Colorado Springs, CO, 20–25 June 2011
Wild, P., Radu, P., Chen, L., Ferryman, J.: Towards anomaly detection for increased security in multibiometric systems: spoofing-resistant 1-median fusion eliminating outliers. In: Proceedings of the 2nd International Joint Conference on Biometrics, pp. 1–6, Clearwater, FL, Sept 29 Oct 2 2014
Wolfstetter, E., Dulleck, U., Inderst, R., Kuhbier, P., Lands-Berger, M.: Stochastic dominance: theory and applications. Humboldt University of Berlin, School of Business and Economics, Berlin (1993)
Yager, N., Dunstone, T.: The biometric menagerie. IEEE Trans. Pattern Anal. Mach. Intell. 32(2), 220–230 (2010)
Zuo, J., Nicolo, F., Schmid, N., Boothapati, S.: Encoding, matching and score normalization for cross spectral face recognition: matching SWIR versus visible data. In: Proceedings of 5th International Conference on Biometrics Theory, Applications and Systems, pp. 203–208, Washington DC, 23–26 Sept 2012
Acknowledgments
The authors would like to thank Prof. Z. Sun and his students for sharing their data. Portions of the research in this paper use the CASIA-IrisV4 collected by the Chinese Academy of Sciences Institute of Automation (CASIA). This research was funded in part by the US Army Research Laboratory (W911NF-13-1-0127) and the UH Hugh Roy and Lillie Cranz Cullen Endowment Fund. All statements of fact, opinion, or conclusions contained herein are those of the authors and should not be construed as representing the official views or policies of the sponsors.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Moutafis, P., Kakadiaris, I.A. (2016). Exploiting Score Distributions for Biometric Applications. In: Bourlai, T. (eds) Face Recognition Across the Imaging Spectrum. Springer, Cham. https://doi.org/10.1007/978-3-319-28501-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-28501-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28499-6
Online ISBN: 978-3-319-28501-6
eBook Packages: Computer ScienceComputer Science (R0)