A Monte Carlo Strategy to Integrate Detection and Model-Based Face Analysis

  • Sandro Schönborn
  • Andreas Forster
  • Bernhard Egger
  • Thomas Vetter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8142)


We present a novel probabilistic approach for fitting a statistical model to an image. A 3D Morphable Model (3DMM) of faces is interpreted as a generative (Top-Down) Bayesian model. Random Forests are used as noisy detectors (Bottom-Up) for the face and facial landmark positions. The Top-Down and Bottom-Up parts are then combined using a Data-Driven Markov Chain Monte Carlo Method (DDMCMC). As core of the integration, we use the Metropolis-Hastings algorithm which has two main advantages. First, the algorithm can handle unreliable detections and therefore does not need the detectors to take an early and possible wrong hard decision before fitting. Second, it is open for integration of various cues to guide the fitting process. Based on the proposed approach, we implemented a completely automatic, pose and illumination invariant face recognition application. We are able to train and test the building blocks of our application on different databases. The system is evaluated on the Multi-PIE database and reaches state of the art performance.


Face Recognition Face Detection Proposal Distribution Monte Carlo Integration Random Forest Algorithm 
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 2013

Authors and Affiliations

  • Sandro Schönborn
    • 1
  • Andreas Forster
    • 1
  • Bernhard Egger
    • 1
  • Thomas Vetter
    • 1
  1. 1.Department for Mathematics and Computer ScienceUniversity of BaselSwitzerland

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