Fusion of visual and infra-red face scores by weighted power series

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5 Citations (Scopus)


This paper proposes a weighted power series model for face verification scores fusion. Essentially, a linear parametric power series model is adopted to directly minimize an approximated total error rate for fusion of multi-modal face verification scores. Unlike the conventional least-squares error minimization approach which involves fitting of a learning model to data density and then perform a threshold process for error counting, this work directly formulates the required target error count rate in terms of design model parameters with a closed-form solution. The solution is found to belong to a specific setting of the weighted least squares. Our experiments on fusing scores from visual and infra-red face images as well as on public data sets show promising results.

Original languageEnglish
Pages (from-to)603-615
Number of pages13
JournalPattern Recognition Letters
Issue number5
Publication statusPublished - 2008 Apr 1

Bibliographical note

Funding Information:
This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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