Approximate Gradient Direction Metric for Face Authentication

  • Josef Kittler
  • Mohammad T. Sadeghi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

In pattern recognition problems where the decision making is based on a measure of similarity, the choice of an appropriate distance metric significantly influences the performance and speed of the decision making process. We develop a novel metric which is an approximation of the successful Gradient Direction (GD) metric. The proposed metric is evaluated on a face authentication problem using the Banca database. It outperforms the standard benchmark, the normalised correlation. Although it is not as powerful as GD metric, it is ten times faster.

Keywords

Face Image Normalise Correlation Neighbourhood Size Gradient Direction False Acceptance Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Josef Kittler
    • 1
  • Mohammad T. Sadeghi
    • 1
  1. 1.Centre for Vision, Speech and Signal Processing, School of Electronics and Physical SciencesUniversity of SurreyGuildfordUK

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