Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/16805
Title: Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition
Authors: Jan, A
Ding, H
Meng, H
Chen, L
Li, H
Keywords: Affective Computing;Facial expression recognition;Human-Computer Interaction
Issue Date: 2018
Citation: 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 2018, pp. 466 - 472 (7)
Abstract: —Meaningful facial parts can convey key cues for both facial action unit detection and expression prediction. Textured 3D face scan can provide both detailed 3D geometric shape and 2D texture appearance cues of the face which are beneficial for Facial Expression Recognition (FER). However, accurate facial parts extraction as well as their fusion are challenging tasks. In this paper, a novel system for 3D FER is designed based on accurate facial parts extraction and deep featurefusionoffacialparts.Experimentsareconductedonthe BU-3DFEdatabase,demonstratingtheeffectivenessofcombing different facial parts, texture and depth cues and reporting the state-of-the-art results in comparison with all existing methods under the same setting.
URI: http://bura.brunel.ac.uk/handle/2438/16805
DOI: http://dx.doi.org/10.1109/FG.2018.00075
ISSN: http://dx.doi.org/10.1109/FG.2018.00075
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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