基于认知注意的多分支自监督学习的微表情检测方法研究

微表情是一种持续时间极短、不易被察觉的面部动作，往往会在高压情况下产生。微表情可以揭示个体隐藏真实的情绪，是非常重要的非语言交流线索，可以被广泛地应用于谎言识别等领域。而微表情检测的研究主要受到小样本问题的限制，本项目将针对这一问题综合使用计算机视觉技术与认知心理学实验方法进行研究。首先，通过基于心理学的认知注意的理论研究和实验验证，构建针对微表情检测的注意力机制。其次，为了解决研究方法受限于标注样本少这一问题，本项目构建多分支自监督学习网络框架，提取视频样本的结构、细节和时空变化特征，并结合深度信息展开多模式研究，最终提出结合认知注意的基于自监督学习的微表情检测方法。该研究将在微表情自动检测领域取得理论和关键技术的突破，提高微表情自动检测的准确性和可靠性，为真实复杂场景下微表情检测的应用奠定了基础。

Micro-expressions are facial movements that are extremely short and not easily perceived, often generated under high pressure. Micro-expressions can reveal the individual's hidden real emotions and are important non-verbal communication clues, widely used in lies detection and other fields. The automatic research of micro-expression spotting is mainly limited by the small sample size. This project will address this problem by comprehensively using computer vision technology and cognitive psychology experimental methods. First, through theoretical research and experimental validation based on cognitive attention, an attentional mechanism is constructed for micro-expression spotting. Second, to solve the problem that the small number of annotated samples limits the research methods, our project constructs a multi-branching self-supervised learning network framework to extract structure, detail, and spatio-temporal variation features of video samples. Besides, we conduct multi-modality research by combining depth information. Finally, our project proposes a micro-expression spotting method based on cognitive attention and multi-branch self-supervised learning. This research will achieve theoretical and technological breakthroughs in the field of automatic micro-expression spotting, improve the accuracy and reliability, and lay the foundation for the application of micro-expression spotting in realistic and complex scenarios.