A Functional Regression Approach to Facial Landmark Tracking
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1332819
- Type
- D - Journal article
- DOI
-
10.1109/TPAMI.2017.2745568
- Title of journal
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Article number
- -
- First page
- 2037
- Volume
- 40
- Issue
- 9
- ISSN
- 0162-8828
- Open access status
- Compliant
- 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)
-
-
- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A Functional Regression approach to Facial Landmark tracking allows incremental face tracking that is so fast, that it's possible, for the first time, to learn to track a previously unseen face on the fly. This is significant because deep-learning approaches are too cumbersome for deployment on e.g. mobile phones. The tracker was licensed to a blue-chip FMCG company as well as two other companies. It is now licensed to university spin-out BlueSkeye AI, who use it to objectively assess mental health status. Contact: Anthony Brown, tosh@blueskeye.com, Co-CEO of BlueSkeye
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
