Face Authentication with Salient Local Features and Static Bayesian Network

  • Guillaume Heusch
  • Sébastien Marcel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


In this paper, the problem of face authentication using salient facial features together with statistical generative models is adressed. Actually, classical generative models, and Gaussian Mixture Models in particular make strong assumptions on the way observations derived from face images are generated. Indeed, systems proposed so far consider that local observations are independent, which is obviously not the case in a face. Hence, we propose a new generative model based on Bayesian Networks using only salient facial features. We compare it to Gaussian Mixture Models using the same set of observations. Conducted experiments on the BANCA database show that our model is suitable for the face authentication task, since it outperforms not only Gaussian Mixture Models, but also classical appearance-based methods, such as Eigenfaces and Fisherfaces.


Bayesian Network Face Recognition Face Image Gaussian Mixture Model Equal Error Rate 
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 2007

Authors and Affiliations

  • Guillaume Heusch
    • 1
    • 2
  • Sébastien Marcel
    • 1
  1. 1.IDIAP Research Institute, rue du Simplon 4, 1920 MartignySwitzerland
  2. 2.Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 LausanneSwitzerland

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