PupilNet: Convolutional Neural Networks for Robust Pupil Detection

Understand

Real-time, accurate, and robust pupil detection is an essential prerequisite for pervasive video-based eye-tracking.

• However, automated pupil detection in real-world scenarios has proven to be an intricate challenge due to fast illumination changes, pupil occlusion, non centered and off-axis eye recording, and physiological eye characteristics.
• In this paper, we propose and evaluate a method based on a novel dual convolutional neural network pipeline.
• In its first stage the pipeline performs coarse pupil position identification using a convolutional neural network and subregions from a downscaled input image to decrease computational costs.