Hybrid Ageing Patterns for face age estimation
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2339
- Type
- D - Journal article
- DOI
-
10.1016/j.imavis.2017.08.005
- Title of journal
- Image and Vision Computing
- Article number
- -
- First page
- 92
- Volume
- 69
- Issue
- -
- ISSN
- 0262-8856
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2017
- URL
-
https://e-space.mmu.ac.uk/619011/
- 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
- Yes
- Number of additional authors
-
3
- Research group(s)
-
B - Human Centred-Computing
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a novel method in face age estimation using multi-scale and multi-resolution facial wrinkle patterns. This research significantly extends previous work by introducing the ability to detect fine and coarse wrinkles. The novelty of this research led to a new collaboration with Image Metrics Ltd (kevin.walker@image-metrics.com) and two successful funding awards from The Royal Society for an Industry Fellowship (IF160006) and a PhD studentship (INF/PHD/180007). These collaborations generated novel methods in face inpainting that is leading to the development of ready to a new ‘real-world’ mobile application supported by Image Metrics.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -