ML Anthology

Face Recognition with Local Binary Patterns

Timo Ahonen, Abdenour Hadid, Matti Pietikäinen
ECCV 2004 pp. 469-481
doi:10.1007/978-3-540-24670-1_36 /eccv/2004/ahonen2004eccv-face/

Abstract

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.

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Cite

Text

Ahonen et al. "Face Recognition with Local Binary Patterns." European Conference on Computer Vision, 2004. doi:10.1007/978-3-540-24670-1_36

Markdown

[Ahonen et al. "Face Recognition with Local Binary Patterns." European Conference on Computer Vision, 2004.](https://mlanthology.org/eccv/2004/ahonen2004eccv-face/) doi:10.1007/978-3-540-24670-1_36

BibTeX

@inproceedings{ahonen2004eccv-face,
  title     = {{Face Recognition with Local Binary Patterns}},
  author    = {Ahonen, Timo and Hadid, Abdenour and Pietikäinen, Matti},
  booktitle = {European Conference on Computer Vision},
  year      = {2004},
  pages     = {469-481},
  doi       = {10.1007/978-3-540-24670-1_36},
  url       = {https://mlanthology.org/eccv/2004/ahonen2004eccv-face/}
}