Efficient privacy-preserving facial expression classification
- Submitting institution
-
City, University of London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 387
- Type
- D - Journal article
- DOI
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10.1109/TDSC.2015.2453963
- Title of journal
- IEEE Transactions on Dependable and Secure Computing
- Article number
- -
- First page
- 326
- Volume
- 14
- Issue
- 3
- ISSN
- 1545-5971
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2015
- 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
-
1
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Facial expression classification (FEC) forms a critical capability desired by human-interacting systems that aim to be responsive to variations in the human’s emotional state. Automatic recognition of faces can be an important component in human-machine interfaces, human emotion analysis and medical care. However, the task of automatically recognizing the various faces is challenging. In addition classifying facial features to protect the identity of the individual is critical to avoid any identity theft. To mitigate these challenges a privacy preserving facial expression classification technique is proposed
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -