Global-local attention for emotion recognition

Le, Nhat, Nguyen, Khanh, Nguyen, Anh ORCID: 0000-0002-1449-211X and Le, Bac (2021) Global-local attention for emotion recognition. NEURAL COMPUTING & APPLICATIONS, 34 (24). pp. 21625-21639.

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Official URL: http://dx.doi.org/10.1007/s00521-021-06778-x

Abstract

<jats:title>Abstract</jats:title><jats:p>Human emotion recognition is an active research area in artificial intelligence and has made substantial progress over the past few years. Many recent works mainly focus on facial regions to infer human affection, while the surrounding context information is not effectively utilized. In this paper, we proposed a new deep network to effectively recognize human emotions using a novel global-local attention mechanism. Our network is designed to extract features from both facial and context regions independently, then learn them together using the attention module. In this way, both the facial and contextual information is used to infer human emotions, therefore enhancing the discrimination of the classifier. The intensive experiments show that our method surpasses the current state-of-the-art methods on recent emotion datasets by a fair margin. Qualitatively, our global-local attention module can extract more meaningful attention maps than previous methods. The source code and trained model of our network are available at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/minhnhatvt/glamor-net">https://github.com/minhnhatvt/glamor-net</jats:ext-link>.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	Emotion recognition, Facial expression recognition, Attention, Deep network
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	20 Jan 2022 11:11
Last Modified:	18 Jan 2023 21:15
DOI:	10.1007/s00521-021-06778-x
Open Access URL:	https://doi.org/10.1007/s00521-021-06778-x
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3147258