Improving Classification with Class-Independent Quality Measures: Q-stack in Face Verification

  • Krzysztof Kryszczuk
  • Andrzej Drygajlo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


Existing approaches to classification with signal quality measures make a clear distinction between the single- and multiple classifier scenarios. This paper presents an uniform approach to dichotomization based on the concept of stacking, Q-stack, which makes use of class-independent signal quality measures and baseline classifier scores in order to improve classification in uni- and multimodal systems alike. In this paper we demonstrate the application of Q-stack on the task of biometric identity verification using face images and associated quality measures. We show that the use of the proposed technique allows for reducing the error rates below those of baseline classifiers in single- and multi-classifier scenarios. We discuss how Q-stack can serve as a generalized framework in any single, multiple, and multimodal classifier ensemble.


statistical pattern classification quality measures confidence measures classifier ensembles stacking 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Krzysztof Kryszczuk
    • 1
  • Andrzej Drygajlo
    • 1
  1. 1.Swiss Federal Institute of Technology Lausanne (EPFL), Signal Processing Institute 

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