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    3D facial expression recognition based on histograms of surface differential quantities

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    Type
    Conference Paper
    Authors
    Li, Huibin
    Morvan, Jean-Marie
    Chen, Liming
    KAUST Department
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2011
    Permanent link to this record
    http://hdl.handle.net/10754/564341
    
    Metadata
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    Abstract
    3D face models accurately capture facial surfaces, making it possible for precise description of facial activities. In this paper, we present a novel mesh-based method for 3D facial expression recognition using two local shape descriptors. To characterize shape information of the local neighborhood of facial landmarks, we calculate the weighted statistical distributions of surface differential quantities, including histogram of mesh gradient (HoG) and histogram of shape index (HoS). Normal cycle theory based curvature estimation method is employed on 3D face models along with the common cubic fitting curvature estimation method for the purpose of comparison. Based on the basic fact that different expressions involve different local shape deformations, the SVM classifier with both linear and RBF kernels outperforms the state of the art results on the subset of the BU-3DFE database with the same experimental setting. © 2011 Springer-Verlag.
    Citation
    Li, H., Morvan, J.-M., & Chen, L. (2011). 3D Facial Expression Recognition Based on Histograms of Surface Differential Quantities. Lecture Notes in Computer Science, 483–494. doi:10.1007/978-3-642-23687-7_44
    Publisher
    Springer Nature
    Journal
    Advanced Concepts for Intelligent Vision Systems
    Conference/Event name
    13th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2011
    ISBN
    9783642236860
    DOI
    10.1007/978-3-642-23687-7_44
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-642-23687-7_44
    Scopus Count
    Collections
    Conference Papers; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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