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).

引入一种新颖的方法，结合了微分几何、核平滑和谱分析，以量化面部肌肉活动，用于广泛可访问的视频录制，如个人智能手机。该方法强调实用性和可访问性，具有在国家安全和整形外科领域的潜在应用。此外，它还提供了中风、贝尔氏麻痹和听神经瘤等疾病的远程诊断和监测，并且能够准确识别和分类从明显到微妙的情绪。所提出的面部肌肉分析技术是对深度学习方法的可解释性替代和面部肌肉电图（fEMG）的非侵入性替代。