Face Recognition with Local Binary Patterns

  • Timo Ahonen
  • Abdenour Hadid
  • Matti Pietikäinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3021)


In this work, we present a novel approach to face recognition which considers both shape and texture information to represent face images. The face area is first divided into small regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a single, spatially enhanced feature histogram efficiently representing the face image. The recognition is performed using a nearest neighbour classifier in the computed feature space with Chi square as a dissimilarity measure. Extensive experiments clearly show the superiority of the proposed scheme over all considered methods (PCA, Bayesian Intra/extrapersonal Classifier and Elastic Bunch Graph Matching) on FERET tests which include testing the robustness of the method against different facial expressions, lighting and aging of the subjects. In addition to its efficiency, the simplicity of the proposed method allows for very fast feature extraction.


Face Recognition Linear Discriminant Analysis Recognition Rate Face Image Local Binary Pattern 
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 2004

Authors and Affiliations

  • Timo Ahonen
    • 1
  • Abdenour Hadid
    • 1
  • Matti Pietikäinen
    • 1
  1. 1.Machine Vision Group,Infotech OuluUniversity of OuluFinland

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