Evaluation of Illumination Compensation Approaches for ELGBPHS

  • Matthias Fischer
  • Marlies Rybnicek
  • Christoph Fischer
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)


Various environmental conditions like pose variations, scale, noise and illumination changes cause matching problems for face recognition algorithms due to the fact that inappropriate data from images is extracted and consequently the recognition rate suffers. In the worst case, persons who should be accepted are rejected and vice versa. Enhanced Local Gabor Binary Patterns Histogram Sequence (ELGBPHS) is considered as an advanced and robust face recognition method. In this paper we evaluated if state-of-the-art illumination compensation approaches can further improve the performance of ELGBPHS. The paper outlines if it is worth to additionally implement preprocessing steps with the increasing complexity and cost. Therefore tests were performed to check if the recognition rate improves if applying preprocessing steps and adjusting essential parameters. Multi-Scale-Retinex, Histogram Equalization, 2D discrete Wavelet-Transformation and one approach combining Gamma Correction, Difference of Gaussian Filtering and Contrast Equalization (TT) were implemented and evaluated.


Face Recognition Recognition Rate Face Image Local Binary Pattern Face Detection 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Matthias Fischer
    • 1
  • Marlies Rybnicek
    • 1
  • Christoph Fischer
    • 1
  1. 1.Institute of IT-Security ResearchSt. Poelten University of Applied SciencesSt. PoeltenAustria

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