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A semi-automatic methodology for facial landmark annotation

Sagonas, Christos; Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja


Christos Sagonas

Georgios Tzimiropoulos

Stefanos Zafeiriou

Maja Pantic


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 resources/facial-point-annotations/. Finally, we present experiments which verify the accuracy of produced annotations.

Start Date Jun 23, 2013
Publication Date Jun 1, 2013
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
APA6 Citation Sagonas, C., Tzimiropoulos, G., & Zafeiriou, S. (2013). A semi-automatic methodology for facial landmark annotation
Keywords Face recognition, Image retrieval, Visual databases
Publisher URL
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


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