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%.
Type de document :
Communication dans un congrès
International Conference on Pattern Recognition (ICPR), Aug 2010, Istanbul, Turkey. IEEE, pp.3724-3727, 2010, <10.1109/ICPR.2010.907>
https://hal.archives-ouvertes.fr/hal-01381501
Contributeur : Équipe Gestionnaire Des Publications Si Liris <>
Soumis le : vendredi 14 octobre 2016 - 14:47:08
Dernière modification le : samedi 15 octobre 2016 - 01:05:31
Xi Zhao, Di Huang, Emmanuel Dellandréa, Liming Chen. Automatic 3D Facial Expression Recognition based on a Bayesian Belief Net and a Statistical Facial Feature Model. International Conference on Pattern Recognition (ICPR), Aug 2010, Istanbul, Turkey. IEEE, pp.3724-3727, 2010, <10.1109/ICPR.2010.907>. <hal-01381501>
