A semi-automatic methodology for facial landmark annotation
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 t...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/31432/ |
| _version_ | 1848794200324177920 |
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| author | Sagonas, Christos Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja |
| author_facet | Sagonas, Christos Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja |
| author_sort | Sagonas, Christos |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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 landmarks 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. |
| first_indexed | 2025-11-14T19:12:25Z |
| format | Conference or Workshop Item |
| id | nottingham-31432 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T19:12:25Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-314322019-03-07T14:17:43Z https://eprints.nottingham.ac.uk/31432/ A semi-automatic methodology for facial landmark annotation Sagonas, Christos Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja 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 landmarks 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. 2013-06 Conference or Workshop Item PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/31432/1/tzimiroCVPRW13.pdf Sagonas, Christos, Tzimiropoulos, Georgios, Zafeiriou, Stefanos and Pantic, Maja (2013) A semi-automatic methodology for facial landmark annotation. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPRW), 23-28 June 2013, Portland, Oregon, USA. Face recognition Image retrieval Visual databases http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6595977&newsearch=true&queryText=semi-automatic%20methodology%20for%20facial%20landmark%20annotation |
| spellingShingle | Face recognition Image retrieval Visual databases Sagonas, Christos Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja A semi-automatic methodology for facial landmark annotation |
| title | A semi-automatic methodology for facial landmark annotation |
| title_full | A semi-automatic methodology for facial landmark annotation |
| title_fullStr | A semi-automatic methodology for facial landmark annotation |
| title_full_unstemmed | A semi-automatic methodology for facial landmark annotation |
| title_short | A semi-automatic methodology for facial landmark annotation |
| title_sort | semi-automatic methodology for facial landmark annotation |
| topic | Face recognition Image retrieval Visual databases |
| url | https://eprints.nottingham.ac.uk/31432/ https://eprints.nottingham.ac.uk/31432/ |