# Eye-focused Detection of Bell's Palsy in Videos

arXiv: https://arxiv.org/abs/2201.11479 · HTML: https://mlsanity.com/paper/2201.11479/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 27 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks](https://mlsanity.com/paper/1905.13607/) (2019)
- [Detection of Gait Abnormalities caused by Neurological Disorders](https://mlsanity.com/paper/2008.06861/) (2020)

## Built on by (most cited first, 1 of 1)

- [SymFace: Additional Facial Symmetry Loss for Deep Face Recognition](https://mlsanity.com/paper/2409.11816/) (2024)

## Abstract (arXiv)

In this paper, we present how Bell's Palsy, a neurological disorder, can be detected just from a subject's eyes in a video. We notice that Bell's Palsy patients often struggle to blink their eyes on the affected side. As a result, we can observe a clear contrast between the blinking patterns of the two eyes. Although previous works did utilize images/videos to detect this disorder, none have explicitly focused on the eyes. Most of them require the entire face. One obvious advantage of having an eye-focused detection system is that subjects' anonymity is not at risk. Also, our AI decisions based on simple blinking patterns make them explainable and straightforward. Specifically, we develop a novel feature called blink similarity, which measures the similarity between the two blinking patterns. Our extensive experiments demonstrate that the proposed feature is quite robust, for it helps in Bell's Palsy detection even with very few labels. Our proposed eye-focused detection system is not only cheaper but also more convenient than several existing methods.