Face-GPS：一种用于量化视频中面部肌肉动态的综合技术
Face-GPS: A Comprehensive Technique for Quantifying Facial Muscle Dynamics in Videos
AI总结：
本文提出Face-GPS技术，结合微分几何、核平滑与谱分析，从普通视频中量化面部肌肉动态，可应用于国家安全、整形外科及面神经疾病远程诊断，并作为深度学习和fEMG的可解释、非侵入性替代方案。
摘要：
我们提出了一种新方法，该方法结合微分几何、核平滑和谱分析，从广泛可获取的视频记录（例如个人智能手机拍摄的视频）中量化面部肌肉活动。我们的方法强调实用性和可及性，在国家安全和整形外科领域具有重要应用潜力。此外，它还能为中风、贝尔氏麻痹（Bell's palsy）和听神经瘤等医学病症提供远程诊断与监测。而且，该方法擅长检测和分类情绪，从明显的情绪到细微的情绪均能处理。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，也是面部肌电图（fEMG）的非侵入性替代方法。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
