Sebastian Sudholt, Leonard Rothacker and Gernot A. Fink
Proc. 36th German Conference on Pattern Recognition, 2014.
Muenster, Germany
Post processing pattern recognition results has long been a 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 a approach to Meta Recognition using a kernel density estimation. We show it to be able to outperform the aforementioned post processing technique in different scenarios.