Abstract
This paper addresses the problem of predicting recognition performance on a large population from a small gallery. Unlike the current approaches based on a binomial model that use match and non-match scores, this paper presents a generalized two-dimensional model that integrates a hypergeometric probability distribution model explicitly with a binomial model. The distortion caused by sensor noise, feature uncertainty, feature occlusion and feature clutter in the gallery data is modeled. The prediction model provides performance measures as a function of rank, population size and the number of distorted images. Results are shown on NIST-4 fingerprint database and 3D ear database for various sizes of gallery and the population.
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Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 2nd edn. Academic Press, London (2003)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, New York (2003)
Bhanu, B., Wang, R., Tan, X.: Predicting fingerprint recognition performance from a small gallery. In: ICPR Workshop on Biometrics: Challenges arising from Theory to Practice, pp. 47–50 (2004)
Tan, X., Bhanu, B.: On the fundamental performance for fingerprint matching. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 18–20 (2003)
Hong, E.S., Bhanu, B., Jones, G., Qian, X.B.: Performance modeling of vote-based object recognition. In: Proceedings Radar Sensor Technology IX, August 2003, vol. 5077, pp. 157–166 (2003)
Johnson, Y., Sun, J., Bobick, A.F.: Predicting large population data cumulative match characteristic performance from small population data. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 821–829. Springer, Heidelberg (2003)
Wayman, J.L.: Error-rate equations for the general biometric system. IEEE Robotics & Automation Magazine 6(1), 35–48 (1999)
Daugman, J.: The importance of being random: statistical principles of iris recognition. Pattern Recognition 36(2), 279–291 (2003)
Phillips, P.J., Grother, P., Micheals, R.J., Blackburn, D.M., Tabassi, E., Bone, M.: Face Recognition Vendor Test 2002. Evaluation Report (March 2003)
Johnson, Y., Sun, J., Boick, A.F.: Using similarity scores from a small gallery to estimate recognition performance for large galleries. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pp. 100–103 (2003)
Grother, P., Phillips, P.J.: Models of large population recognition performance. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 68–75 (2004)
Tan, X., Bhanu, B.: Robust fingerprint identification. In: Proc. IEEE International Conference on Image Processing (ICIP), vol. 1, pp. 277–280 (2002)
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Wang, R., Bhanu, B., Chen, H. (2005). An Integrated Prediction Model for Biometrics. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_37
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DOI: https://doi.org/10.1007/11527923_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27887-0
Online ISBN: 978-3-540-31638-1
eBook Packages: Computer ScienceComputer Science (R0)