Automatic 3D Facial Expression Recognition based on a Bayesian Belief Net and a Statistical Facial Feature Model
| Autor: | Di Huang, Xi Zhao, Emmanuel Dellandréa, Liming Chen |
|---|---|
| Přispěvatelé: | Extraction de Caractéristiques et Identification (imagine), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2) |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Facial expression
Computer science business.industry Bayesian probability ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering Pattern recognition 02 engineering and technology Facial recognition system Feature model Face (geometry) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Artificial intelligence business |
| Zdroj: | International Conference on Pattern Recognition (ICPR) International Conference on Pattern Recognition (ICPR), Aug 2010, Istanbul, Turkey. pp.3724-3727, ⟨10.1109/ICPR.2010.907⟩ ICPR |
| DOI: | 10.1109/ICPR.2010.907⟩ |
| Popis: | International audience; 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%. |
| Databáze: | OpenAIRE |
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