3D Facial Landmark Detection: How to Deal with Head Rotations?

  • Anke Schwarz
  • Esther-Sabrina Wacker
  • Manuel Martin
  • M. Saquib Sarfraz
  • Rainer Stiefelhagen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

Abstract

3D facial landmark detection is important for applications like facial expression analysis and head pose estimation. However, accurate estimation of facial landmarks in 3D with head rotations is still challenging due to perspective variations. Current state-of-the-art methods are based on random forests. These methods rely on a large amount of training data covering the whole range of head rotations. We present a method based on regression forests which can handle rotations even if they are not included in the training data. To achieve this, we modify both the weak predictors of the tree and the leaf node regressors to adapt to head rotations better. Our evaluation on two benchmark datasets, Bosphorus and FRGC v2, shows that our method outperforms state-of-the-art methods with respect to head rotations, if trained solely on frontal faces.

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Anke Schwarz
    • 1
    • 2
  • Esther-Sabrina Wacker
    • 2
  • Manuel Martin
    • 3
  • M. Saquib Sarfraz
    • 1
  • Rainer Stiefelhagen
    • 1
  1. 1.Karlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.Robert Bosch GmbHStuttgartGermany
  3. 3.Fraunhofer IOSBKarlsruheGermany

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