300-W
2D Keypoints
Face
|...
许可协议: Custom

Overview

The first Automatic Facial Landmark Detection in-the-Wild Challenge (300-W 2013) to be held in conjunction with International Conference on Computer Vision 2013, Sydney, Australia.

Automatic facial landmark detection is a longstanding problem in computer vision, and 300-W Challenge is the first event of its kind organized exclusively to benchmark the efforts in the field. The particular focus is on facial landmark detection in real-world datasets of facial images captured in-the-wild. The results of the Challenge will be presented at the 300-W Faces in-the-Wild Workshop to be held in conjunction with ICCV 2013.

A special issue of Image and Vision Computing Journal will present the best performing methods and summarize the results of the Challenge.

Data Annotation

Existing facial databases cover large variations including: different subjects, poses, illumination, occlusions etc. However, the provided annotations appear to have several limitations.

camera

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

  1. The majority of existing databases provide annotations for a relatively small subset of the overall images.
  2. The accuracy of provided annotations in some cases is not so good (probably due to human fatigue).
  3. The annotation model of each database consists of different number of landmarks.

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).

camera

Figure 2: The 68 points mark-up used for our annotations.

Citation

Please cite as:

@article{SAGONAS20163,
title = "300 Faces In-The-Wild Challenge: database and results",
journal = "Image and Vision Computing",
volume = "47",
pages = "3 - 18",
year = "2016",
note = "300-W, the First Automatic Facial Landmark Detection in-the-Wild Challenge",
issn = "0262-8856",
doi = "https://doi.org/10.1016/j.imavis.2016.01.002",
url = "http://www.sciencedirect.com/science/article/pii/S0262885616000147",
author = "Christos Sagonas and Epameinondas Antonakos and Georgios Tzimiropoulos and Stefanos
Zafeiriou and Maja Pantic",
keywords = "Facial landmark localization, Challenge, Semi-automatic
annotation tool, Facial database",
}
@INPROCEEDINGS{6595977,
author={C. {Sagonas} and G. {Tzimiropoulos} and S. {Zafeiriou} and M. {Pantic}},
booktitle={2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops},
title={A Semi-automatic Methodology for Facial Landmark
Annotation}, year={2013}, volume={}, number={}, pages={896-903},
}
@INPROCEEDINGS{6755925,
author={C. {Sagonas} and
G. {Tzimiropoulos} and S. {Zafeiriou} and M. {Pantic}},  booktitle={2013 IEEE International
Conference on Computer Vision Workshops},
title={300 Faces in-the-Wild Challenge: The First
Facial Landmark Localization Challenge}, year={2013}, volume={}, number={}, pages={397-403},
}

License

Custom

数据概要
数据格式
Image,
数据量
--
文件大小
1.99GB
发布方
ibug
The core expertise of the iBUG group is the machine analysis of human behaviour in space and time including face analysis, body gesture analysis, visual, audio, and multimodal analysis of human behaviour, and biometrics analysis. Application areas in which the group is working are face analysis, body gesture analysis, audio and visual human behaviour analysis, biometrics and behaviometrics, and HCI.
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