Micro-gesture Recognition: A Comprehensive Survey of Datasets, Methods, and Challenges

Micro-gesture recognition (MGR) has recently emerged as an important research direction in affective computing and human-computer interaction, aiming to decode subtle and unconscious bodily movements that reflect hidden emotions. Unlike illustrative gestures, which are intentional, expressive, and long in duration, micro-gestures are subtle, spontaneous, and short-lived, making their recognition far more challenging. MGR has made remarkable progress with the emergence of several public datasets. However, existing reviews mostly focus on conventional gesture or facial micro-expression analysis, leaving MGR as a distinct field that is insufficiently summarized. In this paper, we present the first comprehensive survey of the MGR method. It covers several key aspects: 1) datasets of two diverse modalities and their collection protocols; 2) recognition methods across supervised, unsupervised, contrastive, multimodal fusion, and multimodal large language model (MLLM) paradigms; and 3) challenges such as long-tail distribution, cross-dataset generalization, and bridging recognition with emotion understanding. This survey aims to provide both an overview and future perspectives to advance the development of micro-gesture recognition. Our project is available at Github: https://github.com/timwang2001/Awesome_Micro_Gesture.