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We introduce a novel method that combines differential geometry, kernelssmoothing, and spectral analysis to quantify facial muscle activity from widelyaccessible video recordings, such as those captured on personal smartphones.Our approach emphasizes practicality and accessibility. It has significantpotential for applications in national security and plastic surgery.Additionally, it offers remote diagnosis and monitoring for medical conditionssuch as stroke, Bell's palsy, and acoustic neuroma. Moreover, it is adept atdetecting and classifying emotions, from the overt to the subtle. The proposedface muscle analysis technique is an explainable alternative to deep learningmethods and a non-invasive substitute to facial electromyography (fEMG).

Face-GPS: A Comprehensive Technique for Quantifying Facial Muscle Dynamics in Videos