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Facial Landmarks Localization Estimation by Cascaded Boosted Regression

  • Louis ChevallierEmail author
  • Jean-Ronan Vigouroux
  • Alix Goguey
  • Alexey Ozerov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 458)

Abstract

Accurate detection of facial landmarks is very important for many applications like face recognition or analysis. In this paper we describe an efficient detector of facial landmarks based on a cascade of boosted regressors of arbitrary number of levels. We define as many regressors as landmarks and we train them separately. We describe how the training is conducted for the series of regressors by supplying training samples centered on the predictions of the previous levels. We employ gradient boosted regression and evaluate three different kinds of weak elementary regressors, each one based on Haar features: non parametric regressors, simple linear regressors and gradient boosted trees. We discuss trade-offs between the number of levels and the number of weak regressors for optimal detection speed. Experiments performed on three datasets suggest that our approach is competitive compared to state-of-the art systems regarding precision, speed as well as stability of the prediction on video streams.

Keywords

Face landmarks localization Boosted regression 

Notes

Acknowledgements

This work was partially funded by the QUAERO project supported by OSEO and by the European integrated project AXES.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Louis Chevallier
    • 1
    Email author
  • Jean-Ronan Vigouroux
    • 1
  • Alix Goguey
    • 1
    • 2
  • Alexey Ozerov
    • 1
  1. 1.TechnicolorCesson-SévignéFrance
  2. 2.EnsimagSaint-Martin d’HèresFrance

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