CRED: A Corneal Reflection and Environment Dataset

Existing studies have proved that corneal reflection images can not only visualize the human surroundings, but also accurately reflect the attention information of the eyes to the environment, which promotes the research and application of visual tracking and human posture localization in the field of human-computer interaction. The cornea is a small and transparent reflective surface with weak reflective ability, and its reflected images always have dull colors and low resolution. Some researches try to obtain clearer and brighter corneal reflection images when a person is facing a screen or outdoors, but the reflected images are highly susceptible to the interference of iris color and texture. However, strong corneal reflections are highly susceptible to obscuring the iris and pupil regions, affecting the accuracy of gaze tracking. In addition, a large number of reflected images interfered by the iris must rely on iris features for image enhancement. These two limitations make it difficult to directly apply eye images taken outdoors. We try to propose a corneal reflection and human eye surroundings dataset, CRED, which contains not only segmented images of human eye images and ocular structures (e.g., iris, pupil, and eyelid margins) with significant corneal reflections, but also corneal reflections and ground truth of the human eye surroundings scene separated from the ocular images. We believe that with the help of the CRED dataset, a large number of deep learning-based end-to-end works can be performed for iris and pupil position estimation and localization in the presence of strong corneal reflection interference. Similarly, the clarity and usability of corneal reflection images will be significantly improved.