An automatic 3D facial landmarking algorithm using 2D gabor wavelets

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*

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    23 Citations (Scopus)


    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


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


    Dive into the research topics of 'An automatic 3D facial landmarking algorithm using 2D gabor wavelets'. Together they form a unique fingerprint.

    Cite this