Eye-focused Detection of Bell's Palsy in Videos

TL;DR

This paper introduces an eye-focused method for detecting Bell's Palsy in videos by analyzing blinking patterns, offering a more privacy-preserving, explainable, and cost-effective alternative to face-based detection methods.

Contribution

The paper proposes a novel eye-focused detection system utilizing a new feature called blink similarity, which improves Bell's Palsy detection robustness with minimal labeled data.

Findings

Blink similarity is effective for Bell's Palsy detection.

Eye-focused approach preserves subject anonymity.

System performs well with limited labeled data.

Abstract

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.