• Markus A. De Jong
  • Andreas Wollstein
  • Clifford Ruff
  • David Dunaway
  • Pirro Hysi
  • Tim Spector
  • Fan Liu
  • Wiro Niessen
  • M.J. Koudstaal
  • Manfred Kayser
  • Eppo B. Wolvius
  • Stefan Böhringer

In this paper, we present a novel approach to automatic 3D facial landmarking using 2D Gabor wavelets. Our algorithm considers the face to be a surface and uses map projections to derive 2D features from raw data. Extracted features include texture, relief map, and transformations thereof. We extend an established 2D landmarking method for simultaneous evaluation of these data. The method is validated by performing landmarking experiments on two data sets using 21 landmarks and compared with an active shape model implementation. On average, landmarking error for our method was 1.9 mm, whereas the active shape model resulted in an average landmarking error of 2.3 mm. A second study investigating facial shape heritability in related individuals concludes that automatic landmarking is on par with manual landmarking for some landmarks. Our algorithm can be trained in 30 min to automatically landmark 3D facial data sets of any size, and allows for fast and robust landmarking of 3D faces.

Original languageEnglish
Article number7312454
Pages (from-to)580-588
Number of pages9
JournalIEEE Transactions on Image Processing
Issue number2
Publication statusPublished - 1 Feb 2016

    Research areas

  • 3D, Algorithm, Automatic landmarking, Face, Gabor filter, Landmarking, Surface data, Wavelet

ID: 23319019