Accurate Landmarking of Three-Dimensional Facial Data in the Presence of Facial Expressions and Occlusions Using a Three-Dimensional Statistical Facial Feature Model

Authors:
Xi Zhao;Emmanuel Dellandrea;Liming Chen;Ioannis A. Kakadiaris
Affiliations:
Computational Biomedicine Laboratory, Department of Computer Science, University of Houston, Houston, TX, USA;Université de Lyon, Centre National de la Recherche Scientifique, Ecole Centrale Lyon, Laboratoire d'InfoRmatique en Image et Systèmes d'information, Unité Mixte de Recherche ...;Université de Lyon, Centre National de la Recherche Scientifique, Ecole Centrale Lyon, Laboratoire d'InfoRmatique en Image et Systèmes d'information, Unité Mixte de Recherche ...;Computational Biomedicine Laboratory, Department of Computer Science, University of Houston, Houston, TX, USA
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2011

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Abstract

Three-dimensional face landmarking aims at automatically localizing facial landmarks and has a wide range of applications (e.g., face recognition, face tracking, and facial expression analysis). Existing methods assume neutral facial expressions and unoccluded faces. In this paper, we propose a general learning-based framework for reliable landmark localization on 3-D facial data under challenging conditions (i.e., facial expressions and occlusions). Our approach relies on a statistical model, called 3-D statistical facial feature model, which learns both the global variations in configurational relationships between landmarks and the local variations of texture and geometry around each landmark. Based on this model, we further propose an occlusion classifier and a fitting algorithm. Results from experiments on three publicly available 3-D face databases (FRGC, BU-3-DFE, and Bosphorus) demonstrate the effectiveness of our approach, in terms of landmarking accuracy and robustness, in the presence of expressions and occlusions.