Pixelwise Local Binary Pattern Models of Faces Using Kernel Density Estimation

  • Timo Ahonen
  • Matti Pietikäinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


Local Binary Pattern (LBP) histograms have attained much attention in face image analysis. They have been successfully used in face detection, recognition, verification, facial expression recognition etc. The models for face description have been based on LBP histograms computed within small image blocks. In this work we propose a novel, spatially more precise model, based on kernel density estimation of local LBP distributions. In the experiments we show that this model produces significantly better performance in the face verification task than the earlier models. Furthermore, we show that the use of weighted information fusion from individual pixels based on a linear support vector machine provides with further improvements in performance.


Face Recognition Facial Image Local Binary Pattern Face Detection Kernel Density Estimation 
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 2009

Authors and Affiliations

  • Timo Ahonen
    • 1
  • Matti Pietikäinen
    • 1
  1. 1.Machine Vision GroupUniversity of OuluFinland

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