  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.
