3D facial landmark detection under large yaw and expression variations

Panagiotis Perakis, Georgios Passalis, Theoharis Theoharis, Ioannis A. Kakadiaris

Research output: Contribution to journalArticlepeer-review

104 Scopus citations

Abstract

A 3D landmark detection method for 3D facial scans is presented and thoroughly evaluated. The main contribution of the presented method is the automatic and pose-invariant detection of landmarks on 3D facial scans under large yaw variations (that often result in missing facial data), and its robustness against large facial expressions. Three-dimensional information is exploited by using 3D local shape descriptors to extract candidate landmark points. The shape descriptors include the shape index, a continuous map of principal curvature values of a 3D object's surface, and spin images, local descriptors of the object's 3D point distribution. The candidate landmarks are identified and labeled by matching them with a Facial Landmark Model (FLM) of facial anatomical landmarks. The presented method is extensively evaluated against a variety of 3D facial databases and achieves state-of-the-art accuracy (4.5-6.3 mm mean landmark localization error), considerably outperforming previous methods, even when tested with the most challenging data.

Original languageEnglish (US)
Article number6361404
Pages (from-to)1552-1564
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume35
Issue number7
DOIs
StatePublished - 2013

Keywords

  • Face models
  • landmark detection
  • shape index
  • spin images

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Fingerprint

Dive into the research topics of '3D facial landmark detection under large yaw and expression variations'. Together they form a unique fingerprint.

Cite this