3D Facial expression recognition based on histograms of surface differential quantities
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Front view vs. side view of facial and postural expressions of emotions in a virtual character
Transactions on edutainment VI
Static and dynamic 3D facial expression recognition: A comprehensive survey
Image and Vision Computing
3D/4D facial expression analysis: An advanced annotated face model approach
Image and Vision Computing
Unconventional approaches for facial animation and tracking
SIGGRAPH Asia 2012 Technical Briefs
A fast and robust feature set for cross individual facial expression recognition
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
A review of motion analysis methods for human Nonverbal Communication Computing
Image and Vision Computing
A neural-AdaBoost based facial expression recognition system
Expert Systems with Applications: An International Journal
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We investigate the problem of facial expression recognition using 3D face data. Our approach is based on local shape analysis of several relevant regions of a given face scan. These regions or patches from facial surfaces are extracted and represented by sets of closed curves. A Riemannian framework is used to derive the shape analysis of the extracted patches. The applied framework permits to calculate a similarity (or dissimilarity) distances between patches, and to compute the optimal deformation between them. Once calculated, these measures are employed as inputs to a commonly used classification techniques such as AdaBoost and Support Vector Machines (SVM). A quantitative evaluation of our novel approach is conducted on a subset of the publicly available BU-3DFE database.