300-W face point annotation data set, which can extract facial features - Intelligent Behavior Understanding Group (iBUG), Department of Computing, Imperial College London

face point annotation

describe:

Existing face databases cover a wide variety, including: different subjects, poses, lighting, occlusions, etc. However, the annotations provided seem to have some limitations.

 

  

 

 Figure 1: (a)-(d) Annotated images from MultiPIE, XM2VTS, AR, FRGC Ver.2 databases, and (e) an example of inaccurate annotations from XM2VTS.

 

1. Most existing databases provide annotations for relatively small subsets of the entire image.
2. In some cases, the accuracy of the provided annotations is not very good (probably due to human fatigue). 
3. The annotation models for each database consist of different numbers of landmarks. 

These issues make cross-database experiments and comparisons between different methods almost infeasible. To overcome these difficulties, we propose a semi-automatic annotation method to annotate massive face datasets. This is the first attempt at creating a tool suitable for annotating massive face databases. 

All annotations are for research purposes only (non-commercial product).

  

 

 Figure 2: 68-point markers for annotation.

 

download:

We use our tool to create annotations for the following databases (following Multi-PIE 68-point notation, see Figure 2):

• 300-W [ Part 1 ][ Part 2 ][ Part 3 ][ Part 4 ]
  Please note that the database is simply divided into 4 smaller parts for easier downloading. In order to create the database, you must unzip part 1 (ie 300w.zip.001) using a file archiver program (eg 7zip).
• XM2VTS
• FRGC version 2
• low power pre-shock
• Helen
• AFW 
• LOVE

 

reference:

Please cite as:

• C. Sagonas, E. Antonikos, G. Tsimiropoulos, S. Zafiriou, M. Pantic. 300 Facing challenges in the wild: databases and results . Image and Visual Computing (IMAVIS), Special Issue on "In-The-Wild" Facial Landmark Localization. 2016.

• C. Sagonas, G. Tsimiropoulos, S. Zafirio, M. Pantic. A Semi-Automatic Approach to Facial Landmark Annotation . Proceedings of IEEE International Conference. Computer Vision and Pattern Recognition (CVPR-W), 5th Symposium on Face and Gesture Analysis and Modeling (AMFG 2013). Oregon, USA, June 2013.

• C. Sagonas, G. Tsimiropoulos, S. Zafirio, M. Pantic. 300 Faces in-the-Wild Challenge: The first facial landmark localization challenge . Proceedings of IEEE International Conference. Computer Vision (ICCV-W), 300-sheet challenge in the wild (300-W). Sydney, Australia, December 2013.

touch:

Christo Sagonas - [email protected]   /  Stefanos Zafeiriou - [email protected]