Learning for Meta-Recognition

@article{Scheirer2012LearningFM,
  title={Learning for Meta-Recognition},
  author={Walter J. Scheirer and Anderson Rocha and Jonathan Parris and Terrance E. Boult},
  journal={IEEE Transactions on Information Forensics and Security},
  year={2012},
  volume={7},
  pages={1214-1224}
}
In this paper, we consider meta-recognition, an approach for postrecognition score analysis, whereby a prediction of matching accuracy is made from an examination of the tail of the scores produced by a recognition algorithm. This is a general approach that can be applied to any recognition algorithm producing distance or similarity scores. In practice, meta-recognition can be implemented in two different ways: a statistical fitting algorithm based on the extreme value theory, and a machine… CONTINUE READING
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