Journal
IEEE TRANSACTIONS ON MULTIMEDIA
Volume 18, Issue 7, Pages 1438-1450Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2016.2557063
Keywords
3D/4D facial expression recognition; muscle movement model (MMM); shape representation
Categories
Funding
- Hong Kong, Macao, and Taiwan Science and Technology Cooperation Program of China [L2015TGA9004]
- National Natural Science Foundation of China [61540048, 61273263, 61421003]
- Specialized Research Fund for the Doctoral Program of Higher Education [20121102120016]
- French research agency, l'Agence Nationale de Recherche (ANR) [ANR-13-CORD-0004-02]
- Fundamental Research Funds for the Central Universities
Ask authors/readers for more resources
Facial expression is an important channel for human nonverbal communication. This paper presents a novel and effective approach to automatic 3D/4D facial expression recognition based on the muscular movement model (MMM). In contrast to most of existing methods, the MMM deals with such an issue in the viewpoint of anatomy. It first automatically segments the input 3D face (frame) by localizing the corresponding points within each muscular region of the reference using iterative closest normal point. A set of features with multiple differential quantities, including coordinate, normal, and shape index values, are then extracted to describe the geometry deformation of each segmented region. Meanwhile, we analyze the importance of these muscular areas, and a score level fusion strategy is exploited to optimize their weights by the genetic algorithm in the learning step. The support vector machine and the hidden Markov model are finally used to predict the expression label in 3D and 4D, respectively. The experiments are conducted on the BU-3DFE and BU-4DFE databases, and the results achieved clearly demonstrate the effectiveness of the proposed method.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available