An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge
This survey paper is significant for researchers in computer vision and affective computing, offering an updated overview of methods and challenges in facial micro-expression analysis.
This paper provides a survey of facial micro-expression analysis, focusing on recent advancements in recognition methods, data generation, and spotting techniques. It reviews approaches such as macro-to-micro adaptation, key apex frame recognition, and facial action unit-based recognition, and discusses synthetic data generation to address data limitations.
Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g. police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation.