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.

人眼图像或视频中的瞳孔检测在许多应用中起着关键作用，例如眼动追踪，糖尿病性视网膜病变筛查，智能家居，虹膜识别等。文献表明，瞳孔检测面临许多并发症，包括光反射，白内障，瞳孔狭窄/扩张时刻，隐形眼镜，眉毛，睫毛，头发条和闭眼。为了应对这些挑战，研究团体一直在努力设计弹性的瞳孔定位方案，以用于使用近红外（NIR）或可见光谱（VS）照明收集的图像/视频数据。这项研究提出了对来自标准来源的众多瞳孔检测方案的严格审查。该评价包括基于机器学习，直方图/阈值，整数微分算子（IDO），霍夫变换等。突出显示了每种方案的可能的利弊。最后，本研究为设计鲁棒的瞳孔检测系统提供了建议。由于瞳孔检测的范围非常广泛，因此，该综述对于相关的研究社区将是一个很好的信息来源。