Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference

  • Conrad Sanderson
  • Brian C. Lovell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


We propose a scalable face matching algorithm capable of dealing with faces subject to several concurrent and uncontrolled factors, such as variations in pose, expression, illumination, resolution, as well as scale and misalignment problems. Each face is described in terms of multi-region probabilistic histograms of visual words, followed by a normalised distance calculation between the histograms of two faces. We also propose a fast histogram approximation method which dramatically reduces the computational burden with minimal impact on discrimination performance. Experiments on the “Labeled Faces in the Wild” dataset (unconstrained environments) as well as FERET (controlled variations) show that the proposed algorithm obtains performance on par with a more complex method and displays a clear advantage over predecessor systems. Furthermore, the use of multiple regions (as opposed to a single overall region) improves accuracy in most cases, especially when dealing with illumination changes and very low resolution images. The experiments also show that normalised distances can noticeably improve robustness by partially counteracting the effects of image variations.


Face Image Gaussian Mixture Model Visual Word Vector Quantiser Face Variation 
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

  • Conrad Sanderson
    • 1
    • 2
  • Brian C. Lovell
    • 1
    • 2
  1. 1.NICTASt LuciaAustralia
  2. 2.School of ITEEThe University of QueenslandAustralia

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