Adaptive 3D facial action intensity estimation and emotion recognition
- Submitting institution
-
Teesside University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 7086205
- Type
- D - Journal article
- DOI
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10.1016/j.eswa.2014.08.042
- Title of journal
- Expert Systems With Applications.
- Article number
- -
- First page
- 1446
- Volume
- 42
- Issue
- 3
- ISSN
- 0957-4174
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2014
- 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
-
2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first article that considered 3D subject-independent dynamic features for robust real-life tasks recognition with subject variation, head movement and illumination change. Other researchers adopted and improved the concept further (e.g., Perez-Gaspar et al, 2016, 10.1016/j.eswa.2016.08.047). Associated contributions of this research work received the ‘Best Conference Paper Award’ in an EU Network funded conference (SKIMA 2012, China) and the ‘Best Demo Award’ in the AAMAS 2016 conference (Australia).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -