The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution
Abstract
In this paper, we present a new benchmark (Menpo benchmark) for facial landmark localisation and summarise the results of the recent competition, so-called Menpo Challenge, run in conjunction to CVPR 2017. The Menpo benchmark, contrary to the previous benchmarks such as 300-W and 300-VW, contains facial images both in (nearly) frontal, as well as in profile pose (annotated with a different markup of facial landmarks). Furthermore, we increase considerably the number of annotated images so that deep learning algorithms can be robustly applied to the problem. The results of the Menpo challenge demonstrate that recent deep learning architectures when trained with the abundance of data lead to excellent results. Finally, we discuss directions for future benchmarks in the topic.
Cite
Text
Zafeiriou et al. "The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017. doi:10.1109/CVPRW.2017.263Markdown
[Zafeiriou et al. "The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017.](https://mlanthology.org/cvprw/2017/zafeiriou2017cvprw-menpo/) doi:10.1109/CVPRW.2017.263BibTeX
@inproceedings{zafeiriou2017cvprw-menpo,
title = {{The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution}},
author = {Zafeiriou, Stefanos and Trigeorgis, George and Chrysos, Grigorios and Deng, Jiankang and Shen, Jie},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2017},
pages = {2116-2125},
doi = {10.1109/CVPRW.2017.263},
url = {https://mlanthology.org/cvprw/2017/zafeiriou2017cvprw-menpo/}
}