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

arXiv: https://arxiv.org/abs/2401.05625 · HTML: https://mlsanity.com/paper/2401.05625/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [A Simple Way to Initialize Recurrent Networks of Rectified Linear Units](https://mlsanity.com/paper/1504.00941/) (2015)
- [Frame attention networks for facial expression recognition in videos](https://mlsanity.com/paper/1907.00193/) (2019)
- [Explainable Face Recognition](https://mlsanity.com/paper/2008.00916/) (2020)
- [Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels](https://mlsanity.com/paper/1412.0265/) (2014)
- [Attention Mesh: High-fidelity Face Mesh Prediction in Real-time](https://mlsanity.com/paper/2006.10962/) (2020)
- [Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models](https://mlsanity.com/paper/2112.04827/) (2021)

## Built on by (most cited first, 3 of 3)

- [Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer](https://mlsanity.com/paper/2408.03320/) (2024)
- [Electromyography-Informed Facial Expression Reconstruction for Physiological-Based Synthesis and Analysis](https://mlsanity.com/paper/2503.09556/) (2025)
- [Empathetic Cascading Networks: A Multi-Stage Prompting Technique for Reducing Social Biases in Large Language Models](https://mlsanity.com/paper/2511.18696/) (2025)

## Abstract (arXiv)

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).