Existing facial databases cover large variations including: different subjects, poses, illumination, occlusions etc. However, the provided annotations appear to have several limitations.
Figure 1: (a)-(d) Annotated images from MultiPIE, XM2VTS, AR, FRGC Ver.2 databases, and (e) examples from XM2VTS with inaccurate annotations.
These problems make cross-database experiments and comparisons between different methods 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.
All the annotations are provided for research purposes ONLY (NO commercial products).
Figure 2: The 68 points mark-up used for our annotations.
We employed our tool for creating annotations (following the Multi-PIE 68 points mark-up, please see Fig. 2) for the following databases:
Please cite as:
C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic. 300 faces In-the-wild challenge: Database and results. Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016.
C. Sagonas, G. Tzimiropoulos, S. Zafeiriou, M. Pantic. A semi-automatic methodology for facial landmark annotation. Proceedings of IEEE Int’l Conf. Computer Vision and Pattern Recognition (CVPR-W), 5th Workshop on Analysis and Modeling of Faces and Gestures (AMFG 2013). Oregon, USA, June 2013.
C. Sagonas, G. Tzimiropoulos, S. Zafeiriou, M. Pantic. 300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge. Proceedings of IEEE Int’l Conf. on Computer Vision (ICCV-W), 300 Faces in-the-Wild Challenge (300-W). Sydney, Australia, December 2013.