| 摘要: |
| 微表情能够揭示人类试图隐藏的真实情感, 在谎言检测、医疗护理、深层次情感识别等领域中具有重大应用价值. 不同于宏表情, 微表情是短暂且细微的面部运动, 微表情数据集构建有着较高的门槛且耗时耗力, 因此微表情面临着数据量小、模型难以捕获细微短暂变化等困难. 微表情分析有着两个重要研究分支: 微表情识别和微表情检测, 目前基于深度学习的方法已经成为研究主流. 针对上述情况, 全面综述近年来基于深度学习的微表情识别和检测代表性工作和研究现状. 首先, 对常用的开源微表情数据集全面进行总结, 然后分别介绍微表情识别和检测的代表性研究工作, 对其进行分类梳理并讨论优点和局限性. 接着, 介绍微表情分析常用的评估协议和指标, 整合讨论各个方法的性能表现. 最后对微表情分析的整体发展状况进行总结并对未来研究方向进行展望. |
| 关键词: 微表情分析 微表情识别 微表情检测 深度学习 计算机视觉 |
| DOI：10.13328/j.cnki.jos.007630 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金(62272364); 陕西省重点研发计划(2024GH-ZDXM-47); 陕西省高等教育教学改革研究项目(23JG003); 西安电子科技大学人工智能赋能研究生教育项目(AIZS2501); 西安电子科技大学2024年研究生教育综合改革专项计划 |
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| Survey on Micro-expression Analysis Based on Deep Learning |
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HU Chen-Xing1, MIAO Qi-Guang1, LIU Can-Yu1, LIU Xu-Jie1, LIU Ru-Yi1, WANG Quan1, YANG Zong-Kai1,2
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1.School of Computer Science and Technology, Xidian University, Xi’an 710126, China;2.Central China Normal University, Wuhan 430079, China
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| Abstract: |
| Micro-expressions can reveal genuine emotions that individuals attempt to conceal and therefore have significant application value in areas such as lie detection, healthcare, and fine-grained emotion recognition. Unlike macro-expressions, micro-expressions are brief and subtle facial movements. In addition, the construction of micro-expression datasets involves high costs and considerable effort, which results in limited data availability and makes it difficult for models to capture subtle and transient facial changes. Micro-expression analysis mainly consists of two important research branches: micro-expression recognition and micro-expression detection. Currently, deep learning-based methods dominate research in this field. Accordingly, this study comprehensively reviews representative works and recent advances in deep learning-based micro-expression recognition and micro-expression detection. Firstly, the study provides a systematic summary of commonly used open-source micro-expression datasets. Secondly, it separately introduces representative research on micro-expression recognition (MER) and micro-expression detection (MED), classifies and organizes these methods, and discusses their respective advantages and limitations. Furthermore, it presents commonly used evaluation protocols and metrics for micro-expression analysis and integrates discussions on the performance of various methods. Finally, it summarizes the overall development of micro-expression analysis and offers insights into future research directions. |
| Key words: micro-expression analysis micro-expression recognition micro-expression detection deep learning computer vision |
