Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels
- Submitting institution
-
The University of Lancaster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 260786153
- Type
- D - Journal article
- DOI
-
10.1109/TIFS.2016.2555792
- Title of journal
- IEEE Transactions on Information Forensics and Security
- Article number
- -
- First page
- 1807
- Volume
- 11
- Issue
- 8
- ISSN
- 1556-6013
- 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
-
4
- Research group(s)
-
B - Data Science
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a novel method called “Many Kernel Discriminant Analysis” for the challenge on transformed/encrypted biometrics. It provides a novel way for the implementation of private biometrics directly in the transformed/encrypted domain, fitting well with the security needs arising from the booming 5G edge computing. The paper was published on IEEE TIFS, a top journal in Forensics & Security. This work was in collaboration with Qatar University and University Central Arkansas. This research led to a successful international bid (total $791k) to Qatar Science Foundation, jointly with Qatar University and University Paris.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
