Identity-Free Artificial Emotional Intelligence via Micro-Gesture Understanding

Identity-Free Artificial Emotional Intelligence via Micro-Gesture Understanding

Článek Scopus

In this work, we focus on a special group of human body language — the micro-gesture (MG), which differs from the range of ordinary illustrative gestures in that they are not intentional behaviors performed to convey information to others, but rather unintentional behaviors driven by inner feelings. This characteristic introduces two novel challenges regarding micro-gestures that are worth rethinking. The first is whether strategies designed for other action recognition are entirely applicable to micro-gestures. The second is whether micro-gestures, as supplementary data, can provide additional insights for emotional understanding. In recognizing micro-gestures, we explore various augmentation strategies that take into account the subtle spatial and brief temporal characteristics of micro-gestures, often accompanied by repetitiveness, to determine more suitable augmentation methods. Considering the significance of temporal domain information for micro-gestures, we introduce a simple and efficient spatiotemporal balancing fusion method. We not only study our method on the considered micro-gesture dataset but also conduct experiments on mainstream gesture/action datasets. The results show that our approach performs well in micro-gesture recognition and on other datasets, achieving state-of-the-art performance compared to previous micro-gesture recognition methods. For emotional understanding based on micro-gestures, we construct complex emotional reasoning scenarios. Our evaluation, conducted with large language models, shows that micro-gestures play a significant and positive role in enhancing comprehensive emotional understanding. We confirm that our new insights contribute to advancing research in micro-gesture and emotional artificial intelligence.

In this work, we focus on a special group of human body language — the micro-gesture (MG), which differs from the range of ordinary illustrative gestures in that they are not intentional behaviors performed to convey information to others, but rather unintentional behaviors driven by inner feelings. This characteristic introduces two novel challenges regarding micro-gestures that are worth rethinking. The first is whether strategies designed for other action recognition are entirely applicable to micro-gestures. The second is whether micro-gestures, as supplementary data, can provide additional insights for emotional understanding. In recognizing micro-gestures, we explore various augmentation strategies that take into account the subtle spatial and brief temporal characteristics of micro-gestures, often accompanied by repetitiveness, to determine more suitable augmentation methods. Considering the significance of temporal domain information for micro-gestures, we introduce a simple and efficient spatiotemporal balancing fusion method. We not only study our method on the considered micro-gesture dataset but also conduct experiments on mainstream gesture/action datasets. The results show that our approach performs well in micro-gesture recognition and on other datasets, achieving state-of-the-art performance compared to previous micro-gesture recognition methods. For emotional understanding based on micro-gestures, we construct complex emotional reasoning scenarios. Our evaluation, conducted with large language models, shows that micro-gestures play a significant and positive role in enhancing comprehensive emotional understanding. We confirm that our new insights contribute to advancing research in micro-gesture and emotional artificial intelligence.

contrastive learning | emotion understanding | large language model | Micro-gesture recognition

contrastive learning | emotion understanding | large language model | Micro-gesture recognition

GAO, R.; LIU, X.; XING, B.; YU, Z.; SCHULLER, B.; KÄLVIÄINEN, H.

01.01.2026

Institute of Electrical and Electronics Engineers Inc.

IEEE Transactions on Affective Computing

04 February 2026

Spojené státy americké

15

https://ieeexplore.ieee.org/document/11372220