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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 2/2023

28.11.2022 | Theoretical Advances

Using attention LSGB network for facial expression recognition

verfasst von: Chan Su, Jianguo Wei, Deyu Lin, Linghe Kong

Erschienen in: Pattern Analysis and Applications | Ausgabe 2/2023

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Abstract

Both the multiple sources of the available in-the-wild datasets and noisy information of images lead to huge challenges for discriminating subtle distinctions between combinations of regional expressions in facial expression recognition (FER). Although deep learning-based approaches have made substantial progresses in FER in recent years, small-scale datasets result in over-fitting during training. To this end, we propose a novel LSGB method which focuses on discriminative attention regions accurately and pretrain the model on ImageNet with the aim of alleviating the problem of over-fitting. Specifically, a more efficient manner combined with a key map, multiple partial maps and a position map is presented in local relation (LR) module to construct higher-level entities through compositional relationship of local pixel pairs. A compact global weighted representation is aggregated by region features, of which the weight is obtained by putting original and regional images to the sequential layer of self-attention module. Finally, extensive experiments are conducted to verify the effectiveness of our proposal. The experimental results on three popular benchmarks demonstrate the superiority of our network with 88.8% on FERplus, 58.68% on AffectNet and 94.9% on JAFFE.
Vorheriger Artikel Polar radius moment with application for affine invariants
Nächster Artikel On improvability of hash clustering data from different sources by bipartite graph

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Metadaten
Titel
Using attention LSGB network for facial expression recognition
verfasst von
Chan Su
Jianguo Wei
Deyu Lin
Linghe Kong
Publikationsdatum
28.11.2022
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 2/2023
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-022-01124-w

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