Issue No. 07 - July (2013 vol. 35)
P. Perakis , Dept. of Inf. & Telecommun., Univ. of Athens, Ilisia, Greece
G. Passalis , Dept. of Inf. & Telecommun., Univ. of Athens, Ilisia, Greece
T. Theoharis , Dept. of Inf. & Telecommun., Univ. of Athens, Ilisia, Greece
I. A. Kakadiaris , Dept. of Comput. Sci., Univ. of Houston, Houston, TX, USA
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.
Shape, Nose, Face, Indexes, Feature extraction, Eigenvalues and eigenfunctions
P. Perakis, G. Passalis, T. Theoharis and I. A. Kakadiaris, "3D Facial Landmark Detection under Large Yaw and Expression Variations," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 7, pp. 1552-1564, 2013.