Abstract
Matching systems can be used in different operation tasks such as verification task and identification task. Different optimization criteria exist for these tasks - reducing cost of acceptance decisions for verification systems and minimizing misclassification rate for identification systems. In this paper we show that the optimal combination rules satisfying these criteria are also different. The difference is caused by the dependence of matching scores produced by a single matcher and assigned to different classes. We illustrate the theory by experiments with biometric matchers and handwritten word recognizers.
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Tulyakov, S., Govindaraju, V., Wu, C. (2007). Optimal Classifier Combination Rules for Verification and Identification Systems. In: Haindl, M., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2007. Lecture Notes in Computer Science, vol 4472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72523-7_39
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DOI: https://doi.org/10.1007/978-3-540-72523-7_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72481-0
Online ISBN: 978-3-540-72523-7
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