Variance-Aware Bi-Attention Expression Transformer for Open-Set Facial Expression Recognition in the Wild Article Swipe
Junjie Zhu
,
B. Luo
,
Ao Sun
,
Jinghang Tan
,
Xibin Zhao
,
Yue Gao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3581783.3612546
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3581783.3612546
Despite the great accomplishments of facial expression recognition (FER) models in closed-set scenarios, they still lack open-world robustness when it comes to handling unknown samples. To address the demands of operating in an open environment, open-set FER models should improve their performance in rejecting unknown samples while maintaining their efficiency in recognizing known expressions. With this goal in mind, we propose an open-set FER framework named Variance-Aware Bi-Attention Expression Transformer (VBExT), which enhances conventional closed-set FER models with open-world robustness for unknown samples. Specifically, to make full use of the expression representation capabilities of learned features, we introduce a bi-attention feature augmentation mechanism that learns the important regions and integrates the hierarchical features extracted by the emotional CNN backbone. We also propose a variance-aware distribution modeling method that adapts to the diverse distribution of different expression classes in the open environment, thereby enhancing the detection ability of unknown expressions. Additionally, we have constructed a Fine-Grained Light Facial Expression dataset that includes 30 different light brightnesses to better validate the efficiency of VBExT. Extensive experiments and ablation studies show that VBExT significantly improves the performance of open-set FER and achieves state-of-the-art results on CFEE (lab, basic), RAF-DB (wild, basic+compound), and FGL-FE (multiple light brightnesses, basic).
Related Topics
Concepts
Robustness (evolution)
Computer science
Facial expression
Open set
Artificial intelligence
Expression (computer science)
Facial expression recognition
Pattern recognition (psychology)
Variance (accounting)
Transformer
Machine learning
Data mining
Facial recognition system
Mathematics
Engineering
Gene
Business
Chemistry
Voltage
Discrete mathematics
Biochemistry
Programming language
Electrical engineering
Accounting
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3581783.3612546
- https://dl.acm.org/doi/pdf/10.1145/3581783.3612546
- OA Status
- gold
- Cited By
- 3
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388186454
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388186454Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/3581783.3612546Digital Object Identifier
- Title
-
Variance-Aware Bi-Attention Expression Transformer for Open-Set Facial Expression Recognition in the WildWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-26Full publication date if available
- Authors
-
Junjie Zhu, B. Luo, Ao Sun, Jinghang Tan, Xibin Zhao, Yue GaoList of authors in order
- Landing page
-
https://doi.org/10.1145/3581783.3612546Publisher landing page
- PDF URL
-
https://dl.acm.org/doi/pdf/10.1145/3581783.3612546Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://dl.acm.org/doi/pdf/10.1145/3581783.3612546Direct OA link when available
- Concepts
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Robustness (evolution), Computer science, Facial expression, Open set, Artificial intelligence, Expression (computer science), Facial expression recognition, Pattern recognition (psychology), Variance (accounting), Transformer, Machine learning, Data mining, Facial recognition system, Mathematics, Engineering, Gene, Business, Chemistry, Voltage, Discrete mathematics, Biochemistry, Programming language, Electrical engineering, AccountingTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2023-10-26 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2963149653, https://openalex.org/W1509031088, https://openalex.org/W2963712289, https://openalex.org/W3134961575, https://openalex.org/W1980331490, https://openalex.org/W4312626952, https://openalex.org/W2896911342, https://openalex.org/W2901114541, https://openalex.org/W3157122767, https://openalex.org/W2194775991, https://openalex.org/W4214756262, https://openalex.org/W2889978276, https://openalex.org/W2940983946, https://openalex.org/W2904483377, https://openalex.org/W2944655310, https://openalex.org/W2895752198, https://openalex.org/W2108287924, https://openalex.org/W2973218493, https://openalex.org/W3184571633, https://openalex.org/W2119880843, https://openalex.org/W4304086144, https://openalex.org/W3014641072, https://openalex.org/W3035336958, https://openalex.org/W4304091652, https://openalex.org/W4304099113, https://openalex.org/W3158594349, https://openalex.org/W4313250949, https://openalex.org/W4304098213, https://openalex.org/W4226489086, https://openalex.org/W4312599584, https://openalex.org/W2904509905, https://openalex.org/W2964347177, https://openalex.org/W2035372623, https://openalex.org/W2139916508, https://openalex.org/W2074356411, https://openalex.org/W2531468424, https://openalex.org/W3206349670, https://openalex.org/W3104842364 |
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