Pupil localization extracts pupil center coordinates from images and videos of the human eye along with the pupillary boundary. Pupil localization essentially plays a major role in identity verification, disease recognition, visual focus of attention (VFOA) tracking, dementia cognitive assessment, and human fatigue detection. However, the process of pupil localization still remains challenging due to various factors, such as poor-quality images, eye makeup, contact lenses, eyelashes, hair strips, eyebrows, closed eyes, and eye saccades. The pupil localization strategies are essentially divided into learning-based and non-learning-based approaches and discussed in detail with the relevant techniques used. This article aims to deliver the essence of current trends in pupil localization and critically discuss the advantages and disadvantages of each method. Hence, this article can be useful to a broad spectrum of readers as a guide to analyzing the latest trends in pupil localization.
