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

Xi Zhao 1 Di Huang 1 Emmanuel Dellandréa 1 Liming Chen 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
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%.
Document type :
Conference papers
Complete list of metadatas
https://hal.archives-ouvertes.fr/hal-01381501
Contributor : Équipe Gestionnaire Des Publications Si Liris <>
Submitted on : Friday, October 14, 2016 - 2:47:08 PM
Last modification on : Thursday, November 21, 2019 - 2:22:11 AM

Identifiers

Collections

CNRS | EC-LYON | UNIV-LYON2 | LIRIS | INSA-LYON | UNIV-LYON1 | INSA-GROUPE | LABEXIMU | UDL

Citation

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. pp.3724-3727, ⟨10.1109/ICPR.2010.907⟩. ⟨hal-01381501⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

147

Contact

Informations

CCSD