Deep Multi-task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature Sharing Article Swipe
Rui Zhao
,
Tianshan Liu
,
Jun Xiao
,
Daniel Pak-Kong Lun
,
Kin‐Man Lam
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.04514
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.04514
Multi-task learning is an effective learning strategy for deep-learning-based facial expression recognition tasks. However, most existing methods take into limited consideration the feature selection, when transferring information between different tasks, which may lead to task interference when training the multi-task networks. To address this problem, we propose a novel selective feature-sharing method, and establish a multi-task network for facial expression recognition and facial expression synthesis. The proposed method can effectively transfer beneficial features between different tasks, while filtering out useless and harmful information. Moreover, we employ the facial expression synthesis task to enlarge and balance the training dataset to further enhance the generalization ability of the proposed method. Experimental results show that the proposed method achieves state-of-the-art performance on those commonly used facial expression recognition benchmarks, which makes it a potential solution to real-world facial expression recognition problems.
Related Topics
Concepts
Computer science
Facial expression
Task (project management)
Multi-task learning
Artificial intelligence
Expression (computer science)
Feature (linguistics)
Generalization
Machine learning
Pattern recognition (psychology)
Feature learning
Transfer of learning
Facial expression recognition
Speech recognition
Facial recognition system
Engineering
Linguistics
Mathematical analysis
Programming language
Mathematics
Systems engineering
Philosophy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2007.04514
- https://arxiv.org/pdf/2007.04514
- OA Status
- green
- Cited By
- 3
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3040980076
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3040980076Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2007.04514Digital Object Identifier
- Title
-
Deep Multi-task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature SharingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-07-09Full publication date if available
- Authors
-
Rui Zhao, Tianshan Liu, Jun Xiao, Daniel Pak-Kong Lun, Kin‐Man LamList of authors in order
- Landing page
-
https://arxiv.org/abs/2007.04514Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2007.04514Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2007.04514Direct OA link when available
- Concepts
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Computer science, Facial expression, Task (project management), Multi-task learning, Artificial intelligence, Expression (computer science), Feature (linguistics), Generalization, Machine learning, Pattern recognition (psychology), Feature learning, Transfer of learning, Facial expression recognition, Speech recognition, Facial recognition system, Engineering, Linguistics, Mathematical analysis, Programming language, Mathematics, Systems engineering, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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41Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2020-07-09 |
| publication_year | 2020 |
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| abstract_inverted_index.of | 104 |
| abstract_inverted_index.on | 118 |
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| abstract_inverted_index.we | 45, 84 |
| abstract_inverted_index.The | 65 |
| abstract_inverted_index.and | 52, 61, 80, 93 |
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