TY - GEN
T1 - EMOPAIN Challenge 2020
T2 - 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020
AU - Egede, Joy O.
AU - Song, Siyang
AU - Olugbade, Temitayo A.
AU - Wang, Chongyang
AU - Williams, Amanda C.De C.
AU - Meng, Hongying
AU - Aung, Min
AU - Lane, Nicholas D.
AU - Valstar, Michel
AU - Bianchi-Berthouze, Nadia
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - The EmoPain 2020 Challenge is the first international competition aimed at creating a uniform platform for the comparison of multi-modal machine learning and multimedia processing methods of chronic pain assessment from human expressive behaviour, and also the identification of pain-related behaviours. The objective of the challenge is to promote research in the development of assistive technologies that help improve the quality of life for people with chronic pain via real-time monitoring and feedback to help manage their condition and remain physically active. The challenge also aims to encourage the use of the relatively underutilised, albeit vital bodily expression signals for automatic pain and pain-related emotion recognition. This paper presents a description of the challenge, competition guidelines, bench-marking dataset, and the baseline systems' architecture and performance on the Challenge's three sub-tasks: pain estimation from facial expressions, pain recognition from multimodal movement, and protective movement behaviour detection.
AB - The EmoPain 2020 Challenge is the first international competition aimed at creating a uniform platform for the comparison of multi-modal machine learning and multimedia processing methods of chronic pain assessment from human expressive behaviour, and also the identification of pain-related behaviours. The objective of the challenge is to promote research in the development of assistive technologies that help improve the quality of life for people with chronic pain via real-time monitoring and feedback to help manage their condition and remain physically active. The challenge also aims to encourage the use of the relatively underutilised, albeit vital bodily expression signals for automatic pain and pain-related emotion recognition. This paper presents a description of the challenge, competition guidelines, bench-marking dataset, and the baseline systems' architecture and performance on the Challenge's three sub-tasks: pain estimation from facial expressions, pain recognition from multimodal movement, and protective movement behaviour detection.
KW - Automatic Pain Assessment
KW - Facial Expression Analysis
KW - Pain related Behaviour Analysis
KW - Protective Movement behaviour Detection
UR - http://www.scopus.com/inward/record.url?scp=85099067829&partnerID=8YFLogxK
U2 - 10.1109/FG47880.2020.00078
DO - 10.1109/FG47880.2020.00078
M3 - Conference contribution
AN - SCOPUS:85099067829
T3 - Proceedings - 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020
SP - 849
EP - 856
BT - Proceedings - 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020
A2 - Struc, Vitomir
A2 - Gomez-Fernandez, Francisco
PB - The Institute of Electrical and Electronics Engineers (IEEE)
Y2 - 16 November 2020 through 20 November 2020
ER -