Face-GPS: A Comprehensive Technique for Quantifying Facial Muscle Dynamics in Videos
This work addresses the need for practical and accessible facial muscle analysis, with applications in national security, plastic surgery, and remote medical diagnosis for conditions like stroke, but it appears incremental as it combines existing techniques like differential geometry and spectral analysis.
The authors tackled the problem of quantifying facial muscle dynamics from widely accessible video recordings, achieving a method that serves as an explainable alternative to deep learning and a non-invasive substitute to facial electromyography.
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).