  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).
