Gauss-Newton deformable part models for face alignment in-the-wild
- Submitting institution
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Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 533
- Type
- E - Conference contribution
- DOI
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10.1109/CVPR.2014.239
- Title of conference / published proceedings
- Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- First page
- 1851
- Volume
- -
- Issue
- -
- ISSN
- 1063-6919
- Open access status
- Out of scope for open access requirements
- Month of publication
- September
- Year of publication
- 2014
- URL
-
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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1
- Research group(s)
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-
- Citation count
- 127
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Paper led to EPSRC project: EP/M02153X/1 (2015-16, £98.6k). Provided the face tracking technology for FP7 FROG (grant no. 288235). It provides the basis for the journal: G. Tzimiropoulos, Pantic “Fast algorithms for fitting active appearance models to unconstrained images,” International Journal of Computer Vision, 2017. It provided the theoretical underpinnings for: (1) G. Tzimiropoulos, “Project-out Cascaded Regression with an application to face alignment,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 and (2) M.H. Khan, J. McDonagh, G. Tzimiropoulos, “Synergy between face alignment and tracking via discriminative global consensus optimization,” IEEE International Conference on Computer Vision (ICCV), 2017.
- Author contribution statement
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- Non-English
- No
- English abstract
- -