The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A Semi-automatic Methodology for Facial Landmark Annotation
Open accessAbstract
Developing powerful deformable face models requires massive, annotated face databases on which techniques can be trained, validated and tested. Manual annotation of each facial image in terms of landmarks requires a trained expert and the workload is usually enormous. Fatigue is one of the reasons that in some cases annotations are inaccurate. This is why, the majority of existing facial databases provide annotations for a relatively small subset of the training images. Furthermore, there is hardly any correspondence between the annotated land-marks across different databases. These problems make cross-database experiments almost infeasible. To overcome these difficulties, we propose a semi-automatic annotation methodology for annotating massive face datasets. This is the first attempt to create a tool suitable for annotating massive facial databases. We employed our tool for creating annotations for MultiPIE, XM2VTS, AR, and FRGC Ver. 2 databases. The annotations will be made publicly available from http://ibug.doc.ic.ac.uk/ resources/facial-point-annotations/. Finally, we present experiments which verify the accuracy of produced annotations.
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Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
Cited by
Bulat, A., & Tzimiropoulos, G. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.116 · Cited by 1,464
Self-citation300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge
Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2013) · 2013 IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.59 · Cited by 1,119
Self-citationFace Alignment at 3000 FPS via Regressing Local Binary Features
Ren, S., Cao, X., Wei, Y., & Sun, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.218 · Cited by 853
AgeDB: The First Manually Collected, In-the-Wild Age Database
Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., & Zafeiriou, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.250 · Cited by 800
Self-citationIncremental Face Alignment in the Wild
Asthana, A., Zafeiriou, S., Cheng, S., & Pantic, M. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.240 · Cited by 450
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Metadata
- Authors
- Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M.
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 896-903
- OpenAlex
- W2006902452
- Cited by
- 387
- References
- 1
- FWCI
- 24.53
- Type
- conference paper
- Language
- en
- Open access
- green
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- Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2013). A Semi-automatic Methodology for Facial Landmark Annotation. 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops. https://doi.org/10.1109/cvprw.2013.132
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