Facial expression-aware face frontalization
- Submitting institution
-
University of Portsmouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 15463601
- Type
- E - Conference contribution
- DOI
-
10.1007/978-3-319-54187-7_25
- Title of conference / published proceedings
- LNCS Proceedings of ACCV16
- First page
- 375
- Volume
- -
- Issue
- -
- ISSN
- 0302-9743
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2017
- 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
-
3
- Research group(s)
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B - Computational Intelligence
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Addresses one of the key challenges for in-the-wild facial expression recognition, that is, how to tackle the variations of out-of-plane head rotation. The presented novel method is based on face frontalisation and outperforms the state-of-the-art view-invariant facial expression recognition methods. Importantly, the method implements 2D face frontalisation, which is less sensitive to the quality of training data.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -