A semi-automatic methodology for facial landmark annotation

Sagonas, C., Tzimiropoulos, Georgios, Zafeiriou, S. and Pantic, M (2013) A semi-automatic methodology for facial landmark annotation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 23 - 28 June 2013, Portland, OR; United States.

Full text not available from this repository.

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


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. © 2013 IEEE.

Additional Information:Conference Code:99733
Keywords:Face database, Facial images, Facial landmark, Facial point detections, Manual annotation, Semi-automatic annotation, Semi-automatics, Training image, Experiments, Pattern recognition, Tools, Database systems
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:12762
Deposited On:21 Dec 2013 19:57

Repository Staff Only: item control page