We introduce a novel method that combines differential geometry, kernels smoothing, and spectral analysis to quantify facial muscle activity from widely accessible video recordings, such as those captured on personal smartphones. Our approach emphasizes practicality and accessibility. It has significant potential for applications in national security and plastic surgery. Additionally, it offers remote diagnosis and monitoring for medical conditions such as stroke, Bell's palsy, and acoustic neuroma. Moreover, it is adept at detecting and classifying emotions, from the overt to the subtle. The proposed face muscle analysis technique is an explainable alternative to deep learning methods and a non-invasive substitute to facial electromyography (fEMG).

翻译：我们提出了一种新颖方法，结合微分几何、核平滑与频谱分析，从广泛可获取的视频记录（如个人智能手机拍摄的视频）中量化面部肌肉活动。该方法强调实用性与可访问性，在国家安全与整形外科领域具有显著应用潜力，同时可为中风、贝尔氏麻痹及听神经瘤等医疗状况提供远程诊断与监测。此外，该方法能有效检测并分类从显性到微妙的情感。所提出的面部肌肉分析技术是深度学习方法的可解释替代方案，并可作为面部肌电图（fEMG）的无创性替代手段。