Confidence Measure for Experimental Automatic Face Recognition System

  • Pavel KrálEmail author
  • Ladislav Lenc
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8946)


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.


Face recognition Czech News Agency Confidence measure Multi-layer perceptron Scale Invariant Feature Transform (SIFT) 



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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Faculty of Applied SciencesUniversity of West BohemiaPlzeňCzech Republic
  2. 2.New Technologies for the Information Society, Faculty of Applied SciencesUniversity of West BohemiaPlzeňCzech Republic

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