Most of the existing studies on 3D Facial Expression Recognition (FER) are messaged-based
approaches, which only detect the already known six universal expressions. In this paper, we
describe the group of global and local features used to comprehensively characterize facial
activities. These features are further used to train Statistical Feature Models (SFMs) associated
with each Action Unit (AU). The occurrence probability of a specific AU on an input textured
3D face model is then computed. The results demonstrate that the evidence of AUs is of
importance for applying AU space to evaluate expressions.
Training on Statistical Feature Models of Action Units for 3D Facial Expression Recognition
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Pre-published on:
February 26, 2018
Published on:
March 08, 2018
Abstract
DOI: https://doi.org/10.22323/1.300.0021
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Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.