A micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition
- Submitting institution
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University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22063504
- Type
- D - Journal article
- DOI
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10.1109/TCYB.2016.2549639
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 1469
- Volume
- 47
- Issue
- 6
- ISSN
- 2168-2267
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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D - Computer Vision and Natural Computing (CVNC)
- Citation count
- 103
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The significance of this work was recognized by an invitation to be guest editor in a special issue on “Automatic Facial and Bodily Expression Perception for Human Behaviour Understanding” in Springer Multimedia Tools & Applications (https://static.springer.com/sgw/documents/1637459/application/pdf/MTAP+CFP+1121.pdf). Whilst the importance of its conclusion on evolutionary models was highlighted by a special session on Evolving Machine/Deep Learning Models for Computer Vision in the IEEE-sponsored International Joint Conference on Neural Networks (IJCNN2019, Hungary, CORE-A). Its feature selection work underpinned the award of an InnovateUK KTP with Smyths Toys Ltd to develop a set of Machine Learning tools for toy events (ref: 1024955, £140k).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -