Abstract
This paper deals with automatic face recognition in order to propose and implement an experimental face recognition system. It will be used to automatically annotate photographs taken in completely uncontrolled environment. Recognition accuracy of such a system can be improved by identification of incorrectly classified samples in the post-processing step. However, this step is usually missing in current systems. In this work, we would like to solve this issue by proposing and integrating a confidence measure module to identify incorrectly classified examples. We propose a novel confidence measure approach which combines four partial measures by a multi-layer perceptron. Two individual measures are based on the posterior probability and two other ones use the predictor features. The experimental results show that the proposed system is very efficient, because almost all erroneous examples are successfully detected.
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Acknowledgements
This work has been partly supported by the UWB grant SGS-2013-029 Advanced Computer and Information Systems and by the European Regional Development Fund (ERDF), project “NTIS - New Technologies for Information Society”, European Centre of Excellence, CZ.1.05/1.1.00/02.0090. We also would like to thank Czech New Agency (ČTK) for support and for providing the photographic data.
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Král, P., Lenc, L. (2015). Confidence Measure for Experimental Automatic Face Recognition System. In: Duval, B., van den Herik, J., Loiseau, S., Filipe, J. (eds) Agents and Artificial Intelligence. ICAART 2014. Lecture Notes in Computer Science(), vol 8946. Springer, Cham. https://doi.org/10.1007/978-3-319-25210-0_22
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