@Article{HamdiDibeklioglu2012,
author="Hamdi Dibeklioglu
and Albert Ali Salah
and Theo Gevers",
title="A Statistical Method for 2D Facial Landmarking",
journal="IEEE Transactions on Image Processing",
year="2012",
volume="21",
number="2",
pages="844--858",
abstract="IF = 3.32Many facial-analysis approaches rely on robust and accurate automatic facial landmarking to correctly function. In this paper, we describe a statistical method for automatic facial-landmark localization. Our landmarking relies on a parsimonious mixture model of Gabor wavelet features, computed in coarse-to-fine fashion and complemented with a shape prior. We assess the accuracy and the robustness of the proposed approach in extensive cross-database conditions conducted on four face data sets (Face Recognition Grand Challenge, Cohn-Kanade, Bosphorus, and BioID). Our method has 99.33\% accuracy on the Bosphorus database and 97.62\% accuracy on the BioID database on the average, which improves the state of the art. We show that the method is not significantly affected by low-resolution images, small rotations, facial expressions, and natural occlusions such as beard and mustache. We further test the goodness of the landmarks in a facial expression recognition application and report landmarking-induced improvement over baseline on two separate databases for video-based expression recognition (Cohn-Kanade and BU-4DFE).",
optnote="ALTRES;ISE",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1853), last updated on Wed, 21 Jun 2017 13:09:20 +0200",
issn="1057-7149",
doi="10.1109/TIP.2011.2163162"
}