Pupil detection schemes in human eye: a review

Pupil detection in a human eyeimage or video plays a key role in many applications such as eye-tracking, diabetic retinopathy screening, smart homes, iris recognition, etc. Literature reveals pupil detection faces many complications including light reflections, cataract disease, pupil constriction/dilation moments, contact lenses, eyebrows, eyelashes, hair strips, and closed eye. To cope with these challenges, research community has been struggling to devise resilient pupil localization schemes for the image/video data collected using the near-infrared (NIR) or visible spectrum (VS) illumination. This study presents a critical review of numerous pupil detection schemes taken from standard sources. This review includes pupil localization schemes based on machine learning, histogram/thresholding, Integro-differential operator (IDO), Hough transform and among others. The probable pros and cons of each scheme are highlighted. Finally, this study offers recommendations for designing a robust pupil detection system. As scope of pupil detection is very broader, therefore this review would be a great source of information for the relevant research community.