TY - GEN
T1 - MEGC 2019 - The second facial micro-expressions grand challenge
AU - See, John
AU - Yap, Moi Hoon
AU - Li, Jingting
AU - Hong, Xiaopeng
AU - Wang, Su-Jing
N1 - Funding Information:
We would like to thank Image Metrics Ltd for sponsoring the prizes for the challenge winners. The workshop chairs would like to thank their funders: National Natural Science Foundation of China (61772511, 61472138, 61572205), The UK Royal Society Industry Fellowship (IF160006), MOHE Malaysia Grant No. FRGS/1/2016/ICT02/MMU/02/2, Shanghai ’The Belt and Road’ Young Scholar Exchange Grant (17510740100), Academic of Finland, Tekes, Infotech OuluChina scholarship council and ANR Reflet.
Funding Information:
We would like to thank Image Metrics Ltd for sponsoring the prizes for the challenge winners. The workshop chairs would like to thank their funders: National Natural Science Foundation of China (61772511, 61472138, 61572205), The UK Royal Society Industry Fellowship (IF160006), MOHE Malaysia Grant No. FRGS/1/2016/ICT02/MMU/02/2, Shanghai 'The Belt and Road' Young Scholar Exchange Grant (17510740100), Academic of Finland, Tekes, Infotech Oulu China scholarship council and ANR Reflet.
Publisher Copyright:
© 2019 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019/7/11
Y1 - 2019/7/11
N2 - Automatic facial micro-expression (ME) analysis is a growing field of research that has gained much attention in the last five years. With many recent works testing on limited data, there is a need to spur better approaches that are both robust and effective. This paper summarises the 2nd Facial Micro-Expression Grand Challenge (MEGC 2019) held in conjunction with the 14th IEEE Conference on Automatic Face and Gesture Recognition (FG) 2019. In this workshop, we proposed challenges for two micro-expression (ME) tasks-spotting and recognition, with the aim of encouraging rigorous evaluation and development of new robust techniques that can accommodate data captured across a variety of settings. In this paper, we outline the evaluation protocols for the two challenge tasks, the datasets involved, and an analysis of the best performing works from the participating teams, together with a summary of results. Finally, we highlight some possible future directions.
AB - Automatic facial micro-expression (ME) analysis is a growing field of research that has gained much attention in the last five years. With many recent works testing on limited data, there is a need to spur better approaches that are both robust and effective. This paper summarises the 2nd Facial Micro-Expression Grand Challenge (MEGC 2019) held in conjunction with the 14th IEEE Conference on Automatic Face and Gesture Recognition (FG) 2019. In this workshop, we proposed challenges for two micro-expression (ME) tasks-spotting and recognition, with the aim of encouraging rigorous evaluation and development of new robust techniques that can accommodate data captured across a variety of settings. In this paper, we outline the evaluation protocols for the two challenge tasks, the datasets involved, and an analysis of the best performing works from the participating teams, together with a summary of results. Finally, we highlight some possible future directions.
UR - http://www.scopus.com/inward/record.url?scp=85070473721&partnerID=8YFLogxK
U2 - 10.1109/FG.2019.8756611
DO - 10.1109/FG.2019.8756611
M3 - Conference contribution
AN - SCOPUS:85070473721
BT - 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)
PB - IEEE
T2 - 14th IEEE International Conference on Automatic Face and Gesture Recognition 2019
Y2 - 14 May 2019 through 18 May 2019
ER -