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Discriminative Bayesian Active Shape Models

  • Pedro Martins
  • Rui Caseiro
  • João F. Henriques
  • Jorge Batista
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)

Abstract

This work presents a simple and very efficient solution to align facial parts in unseen images. Our solution relies on a Point Distribution Model (PDM) face model and a set of discriminant local detectors, one for each facial landmark. The patch responses can be embedded into a Bayesian inference problem, where the posterior distribution of the global warp is inferred in a ımaximum a posteriori (MAP) sense. However, previous formulations do not model explicitly the covariance of the latent variables, which represents the confidence in the current solution. In our Discriminative Bayesian Active Shape Model (DBASM) formulation, the MAP global alignment is inferred by a Linear Dynamical System (LDS) that takes this information into account. The Bayesian paradigm provides an effective fitting strategy, since it combines in the same framework both the shape prior and multiple sets of patch alignment classifiers to further improve the accuracy. Extensive evaluations were performed on several datasets including the challenging Labeled Faces in the Wild (LFW). Face parts descriptors were also evaluated, including the recently proposed Minimum Output Sum of Squared Error (MOSSE) filter. The proposed Bayesian optimization strategy improves on the state-of-the-art while using the same local detectors. We also show that MOSSE filters further improve on these results.

Keywords

Root Mean Square Linear Dynamical System Kernel Density Esti Active Appearance Model Bayesian Paradigm 
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 2012

Authors and Affiliations

  • Pedro Martins
    • 1
  • Rui Caseiro
    • 1
  • João F. Henriques
    • 1
  • Jorge Batista
    • 1
  1. 1.Institute of Systems and RoboticsUniversity of CoimbraPortugal

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