A multi-biometric iris recognition system based on a deep learning approach
- Submitting institution
-
The University of Bradford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 60
- Type
- D - Journal article
- DOI
-
10.1007/s10044-017-0656-1
- Title of journal
- Pattern Analysis and Applications
- Article number
- -
- First page
- 783
- Volume
- 21
- Issue
- 3
- ISSN
- 1433-7541
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2017
- URL
-
https://link.springer.com/article/10.1007/s10044-017-0656-1
- 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)
-
-
- Citation count
- 31
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Real-time multimodal biometric system is presented here based on deep learning representations for right and left iris images, which is integrated with a ranking-level fusion method. This highly-cited work is significant because it was the cornerstone for a new funded H2020 RISE project” Secure and Wireless Multimodal Biometric Scanning Device for Passenger Verification Targeting Land and Sea Border Control" in 2019. This work forms part of the PhD thesis of Al-Waisy 2018 (Supervisor: Qahwaji) and presented as part of Qahwaji’s keynote speech at the Digital Policing Conference 2020 organised by The United Nations Institute for Training and Research (UNITAR).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
