Automatic 3D Facial Expression Recognition Based on a Bayesian Belief Net and a Statistical Facial Feature Model

Authors:
Xi Zhao;Di Huang;Emmanuel Dellandrea;Liming Chen
Affiliations:
-;-;-;-
Venue:
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Year:
2010

Citing 0
Cited 3

Static and dynamic 3D facial expression recognition: A comprehensive survey

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3D/4D facial expression analysis: An advanced annotated face model approach

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A unified probabilistic framework for automatic 3D facial expression analysis based on a Bayesian belief inference and statistical feature models

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Abstract

Automatic facial expression recognition on 3D face data is still a challenging problem. In this paper we propose a novel approach to perform expression recognition automatically and flexibly by combining a Bayesian Belief Net (BBN) and Statistical facial feature models (SFAM). A novel BBN is designed for the specific problem with our proposed parameter computing method. By learning global variations in face landmark configuration (morphology) and local ones in terms of texture and shape around landmarks, morphable Statistic Facial feature Model (SFAM) allows not only to perform an automatic landmarking but also to compute the belief to feed the BBN. Tested on the public 3D face expression database BU-3DFE, our automatic approach allows to recognize expressions successfully, reaching an average recognition rate over 82%.