Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels
- Submitting institution
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University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22063916
- Type
- D - Journal article
- DOI
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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
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4
- Research group(s)
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E - Intelligent Systems Research Group (iSRG)
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper develops an original concept of dealing with chaotic signal recognition in scrambled domain by using many kernel random discriminate analysis (MK-RDA). This paper is the first to propose such framework that efficiently handles biometric verification for emerging IoT applications with high degree of accuracy. The work resulted from an international collaboration between researcher in UK, USA and Qatar has contributed towards securing Qatar Science Foundation Grant (NPRP No.8–140-2–065). The paper has been cited in many publications from leading research groups.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -