WGGLFA: Wavelet-Guided Global–Local Feature Aggregation Network for Facial Expression Recognition Article Swipe
Ke Dong
,
Xi Li
,
Cong Zhang
,
Zhenhua Xiao
,
Renjuan Nie
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/biomimetics10080495
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/biomimetics10080495
Facial expression plays an important role in human–computer interaction and affective computing. However, existing expression recognition methods cannot effectively capture multi-scale structural details contained in facial expressions, leading to a decline in recognition accuracy. Inspired by the multi-scale processing mechanism of the biological visual system, this paper proposes a wavelet-guided global–local feature aggregation network (WGGLFA) for facial expression recognition (FER). Our WGGLFA network consists of three main modules: the scale-aware expansion (SAE) module, which combines dilated convolution and wavelet transform to capture multi-scale contextual features; the structured local feature aggregation (SLFA) module based on facial keypoints to extract structured local features; and the expression-guided region refinement (ExGR) module, which enhances features from high-response expression areas to improve the collaborative modeling between local details and key expression regions. All three modules utilize the spatial frequency locality of the wavelet transform to achieve high-/low-frequency feature separation, thereby enhancing fine-grained expression representation under frequency domain guidance. Experimental results show that our WGGLFA achieves accuracies of 90.32%, 91.24%, and 71.90% on the RAF-DB, FERPlus, and FED-RO datasets, respectively, demonstrating that our WGGLFA is effective and has more capability of robustness and generalization than state-of-the-art (SOTA) expression recognition methods.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/biomimetics10080495
- https://www.mdpi.com/2313-7673/10/8/495/pdf?version=1753609365
- OA Status
- gold
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412694639
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- OpenAlex ID
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https://openalex.org/W4412694639Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/biomimetics10080495Digital Object Identifier
- Title
-
WGGLFA: Wavelet-Guided Global–Local Feature Aggregation Network for Facial Expression RecognitionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-07-27Full publication date if available
- Authors
-
Ke Dong, Xi Li, Cong Zhang, Zhenhua Xiao, Renjuan NieList of authors in order
- Landing page
-
https://doi.org/10.3390/biomimetics10080495Publisher landing page
- PDF URL
-
https://www.mdpi.com/2313-7673/10/8/495/pdf?version=1753609365Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2313-7673/10/8/495/pdf?version=1753609365Direct OA link when available
- Concepts
-
Computer science, Pattern recognition (psychology), Robustness (evolution), Artificial intelligence, Facial expression, Feature (linguistics), Locality, Wavelet transform, Wavelet, Expression (computer science), Speech recognition, Gene, Programming language, Linguistics, Philosophy, Biochemistry, ChemistryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
- References (count)
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68Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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