MPT: Motion Prompt Tuning for Micro-Expression Recognition

• URL: http://arxiv.org/abs/2508.09446v1
• Date: Wed, 13 Aug 2025 02:57:43 GMT
• Title: MPT: Motion Prompt Tuning for Micro-Expression Recognition
• Authors: Jiateng Liu, Hengcan Shi, Feng Chen, Zhiwen Shao, Yaonan Wang, Jianfei Cai, Wenming Zheng,
• Abstract summary: This paper introduces Motion Prompt Tuning (MPT) as a novel approach to adapting pre-training models for micro-expression recognition (MER)<n>MPT represents a pioneering method for subtle motion prompt tuning. Particularly, we introduce motion prompt generation, including motion magnification and Gaussian tokenization, to extract subtle motions as prompts for LMs.<n>Extensive experiments conducted on three widely used MER datasets demonstrate that our proposed MPT consistently surpasses state-of-the-art approaches.
• Score: 47.62949098749473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Micro-expression recognition (MER) is crucial in the affective computing field due to its wide application in medical diagnosis, lie detection, and criminal investigation. Despite its significance, obtaining micro-expression (ME) annotations is challenging due to the expertise required from psychological professionals. Consequently, ME datasets often suffer from a scarcity of training samples, severely constraining the learning of MER models. While current large pre-training models (LMs) offer general and discriminative representations, their direct application to MER is hindered by an inability to capture transitory and subtle facial movements-essential elements for effective MER. This paper introduces Motion Prompt Tuning (MPT) as a novel approach to adapting LMs for MER, representing a pioneering method for subtle motion prompt tuning. Particularly, we introduce motion prompt generation, including motion magnification and Gaussian tokenization, to extract subtle motions as prompts for LMs. Additionally, a group adapter is carefully designed and inserted into the LM to enhance it in the target MER domain, facilitating a more nuanced distinction of ME representation. Furthermore, extensive experiments conducted on three widely used MER datasets demonstrate that our proposed MPT consistently surpasses state-of-the-art approaches and verifies its effectiveness.

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