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S. Zafeiriou, G. Trigeorgis, G. Chrysos, J. Deng and J. Shen, "The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution," 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, 2017, pp. 2116-2125. doi: 10.1109/CVPRW.2017.263

The menpo facial landmark localisation challenge : a step towards the solution

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Author: Zafeiriou, Stefanos1; Trigeorgis, George1; Chrysos, Grigorios1;
Organizations: 1Department of Computing, Imperial College, London
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.1 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2017
Publish Date: 2019-04-26


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.

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Series: IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops
ISSN: 2160-7508
ISSN-E: 2160-7516
ISSN-L: 2160-7508
ISBN: 978-1-5386-0733-6
ISBN Print: 978-1-5386-0734-3
Pages: 2116 - 2125
Article number: 17138328
DOI: 10.1109/CVPRW.2017.263
Host publication: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 21-26 July 2017 Honolulu, Hawaii
Conference: IEEE Conference on Computer Vision and Pattern Recognition Workshops
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: The work of Stefanos Zafeiriou was partially funded by the FiDiPro program of Tekes (project number: 1849/31/2015), as well as by the EPSRC project EP/N007743/1 (FACER2VM). G. Trigeorgis and G. Chrysos were supported by EPSRC DTA award at Imperial College London. J. Deng was supported by the President’s Scholarship of Imperial College London.
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