Screening for refractive error with low-quality smartphone images

Uncorrected refractive errors can lead to permanent debilitating eye conditions if not corrected in a timely manner. Contemporary diagnostic methods rely on the professional acumen of optometrists and the use of expensive devices, which may not be easily accessible to all. According to the optical principle of photorefraction, refractive error can be estimated based on a relative pupil and crescent size of an eye image taken by a camera from a specified working distance. A low-cost approach would be to leverage smartphones with cameras for this purpose. However, the poor image quality generated from basic smartphones poses a challenge for the current approach as they often fail to accurately distinguish the crescent from the iris. We propose a novel method to detect and accurately measure the iris and crescent from smartphone photos. Based on this method, we further propose a set of features for machine learning to build our refractive error estimation model. The performance of our models are evaluated in an in-depth experiment.