Meta-Auxiliary Learning for Micro-Expression Recognition

arXiv:2404.12024v1 Announce Type: new
Abstract: Micro-expressions (MEs) are involuntary movements revealing people's hidden feelings, which has attracted numerous interests for its objectivity in emotion detection. However, despite its wide applications in various scenarios, micro-expression recognition (MER) remains a challenging problem in real life due to three reasons, including (i) data-level: lack of data and imbalanced classes, (ii) feature-level: subtle, rapid changing, and complex features of MEs, and (iii) decision-making-level: impact of individual differences. To address these issues, we propose a …

abstract applications arxiv cs.cv data detection emotion emotion detection feelings hidden however life meta micro movements people recognition type