Abstract
In this paper, we present a fully automatic approach for identity-independent facial expression recognition from 3D video sequences. Towards this goal, we propose a novel approach to extract a dense scalar field that represents the deformations between faces conveying different expressions. We extract relevant features from this deformation field using LDA and then train a dynamic model on these features using HMM. Experiments conducted on BU-4DFE dataset following state-of-the-art settings show the effectiveness of the proposed approach.
This work is supported by the French Research Agency (ANR) through the 3D Face Analyzer project under the contract ANR 2010 INTB 0301 01.
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Drira, H., Ben Amor, B., Daoudi, M., Berretti, S. (2013). A Dense Deformation Field for Facial Expression Analysis in Dynamic Sequences of 3D Scans. In: Salah, A.A., Hung, H., Aran, O., Gunes, H. (eds) Human Behavior Understanding. HBU 2013. Lecture Notes in Computer Science, vol 8212. Springer, Cham. https://doi.org/10.1007/978-3-319-02714-2_13
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DOI: https://doi.org/10.1007/978-3-319-02714-2_13
Publisher Name: Springer, Cham
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