Abstract
Post processing pattern recognition results has long been an effective way to reduce the false recognitions by rejecting results that are deemed wrong by a verification system. Recent work laid down a theoretical foundation for a specific post recognition approach. This approach was termed Meta Recognition by its inventors and is based on a statistical outlier detection that makes use of the Weibull distribution. Using distance or similarity scores that are generated at recognition time, Meta Recognition automatically classifies a recognition result to be correct or incorrect. In this paper we present a novel approach to Meta Recognition using a kernel density estimation. We show this approach to be able to outperform the aforementioned post processing technique in different scenarios.
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Sudholt, S., Rothacker, L., Fink, G.A. (2014). Kernel Density Estimation for Post Recognition Score Analysis. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_49
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DOI: https://doi.org/10.1007/978-3-319-11752-2_49
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