Facial Landmark Localization Using Robust Relationship Priors and Approximative Gibbs Sampling

  • Karsten VogtEmail author
  • Oliver Müller
  • Jörn Ostermann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)


We tackle the facial landmark localization problem as an inference problem over a Markov Random Field. Efficient inference is implemented using Gibbs sampling with approximated full conditional distributions in a latent variable model. This approximation allows us to improve the runtime performance 1000-fold over classical formulations with no perceptible loss in accuracy. The exceptional robustness of our method is realized by utilizing a \(L_{1}\)-loss function and via our new robust shape model based on pairwise topological constraints. Compared with competing methods, our algorithm does not require any prior knowledge or initial guess about the location, scale or pose of the face.


Gibbs Sampling Appearance Model Unary Potential Landmark Localization Full Conditional Distribution 
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 International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institut Für Informationsverarbeitung (TNT)Leibniz Universität HannoverHanoverGermany

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