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Fusion of Local Descriptors for Multi-view Facial Expression Recognition

Wang, Xuejian, Fairhurst, Michael, Canuto, Anne (2018) Fusion of Local Descriptors for Multi-view Facial Expression Recognition. In: IEEE Conference on Intelligent Systems. . pp. 570-575. IEEE, USA ISBN 978-1-5386-8023-0. (doi:10.1109/BRACIS.2018.00104) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:73716)

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https://doi.org/10.1109/BRACIS.2018.00104

Abstract

Facial expressions can be seen as a form of non-verbal communication as well as a primary means of conveying social information among humans.Automatic facial expression recognition (FER) can be applied to a wide range of scenarios in human-computer interaction, facial animation, entertainment, and psychology studies. For feature representation in a FER system, various texture descriptors have been employed to derive an effective solution for this system. However, these individual texture descriptor-based FER systems have often failed to achieve effective performance in the recognition of facial expressions. In this sense, it is necessary to further improve the general performance of a facial expression recognition system, evaluating different feature representations. In this paper, a novel local descriptor for a facial expression recognition system is proposed, designated the level of difference descriptor (LOD). The main goal is to use this descriptor as a supplement to state-of-the-art local descriptors to further improve the performance of a FER system in terms of classification accuracy. Furthermore, the fusion of various texture features for devising a robust feature representation for multi-view facial expression recognition is presented.

Item Type: Conference or workshop item (UNSPECIFIED)
DOI/Identification number: 10.1109/BRACIS.2018.00104
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Michael Fairhurst
Date Deposited: 01 May 2019 09:40 UTC
Last Modified: 16 Feb 2021 14:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/73716 (The current URI for this page, for reference purposes)
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