Eye centre localisation: An unsupervised modular approach
- Submitting institution
-
University of the West of England, Bristol
- Unit of assessment
- 12 - Engineering
- Output identifier
- 924782
- Type
- D - Journal article
- DOI
-
10.1108/SR-06-2015-0098
- Title of journal
- Sensor Review
- Article number
- -
- First page
- 277
- Volume
- 36
- Issue
- 3
- ISSN
- 0260-2288
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2016
- URL
-
http://www.emeraldinsight.com/doi/abs/10.1108/SR-06-2015-0098
- 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
-
3
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The idea of employing a single camera for accurate and reliable eye centre localisation proposed by this research has led to a follow-on research project (UEDM-0164), funded through a UWE’s Vice Chancellor’s Interdisciplinary Award. This has not only improved eye centre localisation accuracy using state-of-the-art deep learning models, but also investigated the feasibility of employing this method for dementia diagnosis. A conference paper, reporting on the eye centre localisation model, was accepted by the 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC), which Zhang attended as sessional Chair
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
