Personal identification based on multiple keypoint sets of dorsal hand vein images
- Submitting institution
-
University of Central Lancashire
- Unit of assessment
- 12 - Engineering
- Output identifier
- 13139
- Type
- D - Journal article
- DOI
-
10.1049/iet-bmt.2013.0042
- Title of journal
- IET Biometrics
- Article number
- -
- First page
- 234
- Volume
- 3
- Issue
- 4
- ISSN
- 2047-4938
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2014
- 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
-
2
- Research group(s)
-
C - Centre for Applied Digital Signal and Image Processing
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is among several outputs resulting from a collaboration with the North China University of Technology (NCUT) following the award of Distinguished Visiting Professor of Beijing Municipal Higher Education Commission funded under the Outstanding Talents Programme to Shark (PHR20100205). The work was supported by both Beijing Municipal Natural Science Foundation (KZ201410009012) and National Natural Science Foundation of China (NSFC 61271368). The paper reports the state-of-the-art performance of 100% biometric recognition for the NCUT dataset of more than 2000 hand vein images and helped secure further funding from NSFC to continue the research (NSFC 61673021).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
