A literature survey on robust and efficient eye localization in real-life scenarios

Eye localization has gained a wide range of applications in face recognition, gaze estimation, pose estimation, expression analysis, etc. However, due to the high degree of appearance variability of eyes in size, shape, color, texture and various ambient environment changes, this task is challenging. During the last three decades, numerous techniques have been developed to meet these challenges. The goal of this paper is to categorize and evaluate these algorithms in a comprehensive way. We focus on the overall difficulties and challenges in real-life scenarios, and present a detailed review of prominent algorithms from the perspective of learning generalizable, flexible and efficient statistical eye models from a small number of training images. In addition, we organize the discussion of the global aspects of eye localization in uncontrolled environments, towards the development of a robust eye localization system. This paper concludes with several promising directions for future research.