MEGC2024: ACM Multimedia 2024 Facial Micro-Expression Grand Challenge
Year: 2024
Type: conference-paper
Abstract: Facial micro-expressions (MEs) are involuntary spontaneous movements of the face that typically appear in high-stakes situations where a person attempts to conceal a certain emotion from being known. A decade after the inception of the widely used CASME II and SMIC datasets, research in computational analysis of MEs has now advanced toward new pathways, exploring problems crucial to model generalization and real-world practicality. It is often challenging to design robust algorithms or models for spotting micro-expressions due to the high variability across diverse cultural backgrounds. Also, treating spotting and recognition as separate tasks is undesirable when handling long-spanning videos under realistic settings. This Grand Challenge comprises two distinct tracks: the Cross-Cultural Spotting (CCS) track, and the Spot-Then-Recognize (STR) track. All participating solutions submitted their results to a leaderboard, and several submissions performed well surpassing their respective baseline results. More details are available at: https://megc2024.github.io.
Institutions: Heriot-Watt University Malaysia, Chinese Academy of Sciences, Institute of Psychology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Manchester Metropolitan University, University of Malaya, National Taiwan University, Zhejiang University, Harbin Institute of Technology
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4403791372