Abstract
Selecting appropriate thresholds and fusion rules for a system involving multiple biometric verifiers requires knowledge of the match score statistics for each verifier. While this statistical information can often be measured from training data, that data may not be representative of the environment into which each verifier is deployed. To compensate for missing statistics, we present a technique for estimating the error rates of each verifier using decisions made after a system has been deployed. While this post-deployment data lacks class labels, it is guaranteed to be representative. Extracted error rates can be used to select appropriate fusion rules and search for thresholds that meet operational requirements.
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Beattie, M., Kumar, B.V.K., Lucey, S., Tonguz, O.: Combining verification deci¬sions in a multi-vendor environment. In: Audio- and Video-based Biometric Person Authentication, AVBPA (2005)
Kittler, J., Hater, M., Duin, R.: On combining classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 20, 226–239 (1998)
Ross, A., Jain, A.: Information fusion in biometrics. Pattern Recognition Letters 24, 2115–2125 (2003)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39, 1–38 (1977)
Phillips, P., Plynn, P.J., Scruggs, T., Bowyer, K.W., Hoffman, J.C., Marques, K., Min, J., Worek, J.: Overview of the face recognition grand challenge. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2005)
Chen, L., Liao, H., Lin, J., Ko, M., Yu, C.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition 33, 1713–1726 (2000)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 19, 711–720 (1997)
Mak, M., Zhang, W., He, M.: Determination of a priori decision threshold for phrase-prompted speaker verification. In: International Workshop on Multimedia Data Storage, Retrieval, Integration and Applications, pp. 96–103 (2000)
Pierot, J.B., Lindberg, J., Koowaaij, J., Hutter, H.P., Genoud, D., Blomberg, M., Bimbot, F.: A comparison of a priori threshold setting procedures for speaker verification in the cave project. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 1, pp. 125–128 (1998)
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© 2005 Springer-Verlag Berlin Heidelberg
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Beattie, M., Kumar, B.V.K.V., Lucey, S., Tonguz, O.K. (2005). Automatic Configuration for a Biometrics-Based Physical Access Control System. In: Li, S.Z., Sun, Z., Tan, T., Pankanti, S., Chollet, G., Zhang, D. (eds) Advances in Biometric Person Authentication. IWBRS 2005. Lecture Notes in Computer Science, vol 3781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569947_31
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DOI: https://doi.org/10.1007/11569947_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29431-3
Online ISBN: 978-3-540-32248-1
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