MEGC 2018 - Facial Micro-Expression Grand Challenge (MEGC): Methods and Datasets

Facial micro-expressions (MEs) are involuntary movements of the face that occur spontaneously when a person experiences an emotion but attempts to suppress the facial expression, typically found in a high-stakes environment. Computational analysis and automation of tasks on micro-expressions is an emerging area in face research, with a strong interest appearing as recent as 2014. Only a few spontaneously induced facial micro-expression datasets have provided the impetus to advance further from the computational aspect. Particularly comprehensive are two state-of-the art FACS coded datasets: the Chinese Academy of Sciences Micro-Expression Database II (CASME II) and the Spontaneous Micro-Facial Movement Dataset (SAMM). While much research has been done on these datasets individually, there has been no attempts to introduce a more rigorous and realistic evaluation. This is the inaugural workshop with the aim of promoting interactions between researchers and scholars from within this niche area of research, and also those from broader, general areas of computer vision and psychology research. This workshop has two main agenda: 1) To organize the first Grand Challenge for facial micro-expression research, involving CASME II-SAMM cross-database recognition of micro-expression classes; and 2) To solicit original works that address a variety of modern challenges of ME research such as spotting macro-/micro-expressions from long videos, and deep learning techniques.
Organizers:
Moi Hoon Yap, Manchester Metropolitan University, UK, m.yap-AT-mmu.ac.uk
Sujing Wang, Chinese Academy of Sciences, China, wangsujing-AT-psych.ac.cn
John See, Multimedia University, Malaysia, johnsee-AT-mmu.edu.my
Xiaopeng Hong, University of Oulu, Finland, hongxiaopeng.cn-AT-gmail.com
Stefanos Zafeiriou, Imperial College London, UK, s.zafeiriou-AT-imperial.ac.uk
Advisory panel:
Xiaolan Fu, Chinese Academy of Sciences, China
Guoying Zhao, University of Oulu, Finland