Kernel Density Estimation for Post Recognition Score Analysis

摘要

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.