Frontal View Gait Recognition with Fusion of Depth Features from a Time of Flight Camera
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22063029
- Type
- D - Journal article
- DOI
-
10.1109/TIFS.2018.2870594
- Title of journal
- IEEE Transactions on Information Forensics and Security
- Article number
- -
- First page
- 1067
- Volume
- 14
- Issue
- 4
- ISSN
- 1556-6013
- Open access status
- Exception within 3 months of publication
- Month of publication
- September
- Year of publication
- 2018
- 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)
-
E - Intelligent Systems Research Group (iSRG)
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a depth based biometric algorithm capable to recognise people walking towards un-calibrated cameras using a novel 3D shape representation. The project was partially funded by CSIT Queen’s University Belfast (https://www.qub.ac.uk/csit). The significance of the work was evidenced by an invitation for a plenary talk on "Recent Advances on Facial Recognition" (http://www.icdat2020.dz/). The depth features have formed an important part of a funded project from Qatar's Supreme Committee for Delivery & Legacy (Smart Identity Management for Smart Stadia with Application to FIFA 2022 Word Cup Qatar, Ref: C22IA1-0303-150432)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -