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WFLW
2D Keypoints
Face
|...
License: Unknown

Overview

Wider Facial Landmarks in-the-wild (WFLW) contains 10000 faces (7500 for training and 2500
for testing) with 98 fully manual annotated landmarks. Apart from landmark annotation, out
new dataset includes rich attribute annotations, i.e., occlusion, pose, make-up, illumination,
blur and expression for comprehensive analysis of existing algorithms. Compare to previous
dataset, faces in the proposed dataset introduce large variations in expression, pose and occlusion.
We can simply evaluate the robustness of pose, occlusion, and expression on proposed dataset
instead of switching between multiple evaluation protocols in different datasets.

Landmark Definition

img

Multi-View Illustration

img

Citation

Please use the following citation when referencing the dataset:

@inproceedings{wayne2018lab,
 author = {Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
 title = {Look at Boundary: A Boundary-Aware Face Alignment Algorithm},
 booktitle = {CVPR},
 month = June,
 year = {2018}
}
Data Summary
Type
Image,
Amount
10K
Size
724.18MB
Provided by
TNList(Tsinghua National Laboratory for Information Science and Technology)
| Amount 10K | Size 724.18MB
WFLW
2D Keypoints
Face
License: Unknown

Overview

Wider Facial Landmarks in-the-wild (WFLW) contains 10000 faces (7500 for training and 2500
for testing) with 98 fully manual annotated landmarks. Apart from landmark annotation, out
new dataset includes rich attribute annotations, i.e., occlusion, pose, make-up, illumination,
blur and expression for comprehensive analysis of existing algorithms. Compare to previous
dataset, faces in the proposed dataset introduce large variations in expression, pose and occlusion.
We can simply evaluate the robustness of pose, occlusion, and expression on proposed dataset
instead of switching between multiple evaluation protocols in different datasets.

Landmark Definition

img

Multi-View Illustration

img

Citation

Please use the following citation when referencing the dataset:

@inproceedings{wayne2018lab,
 author = {Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
 title = {Look at Boundary: A Boundary-Aware Face Alignment Algorithm},
 booktitle = {CVPR},
 month = June,
 year = {2018}
}
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