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Real-time, accurate, and robust pupil detection is an essential prerequisitefor pervasive video-based eye-tracking. However, automated pupil detection inrealworld scenarios has proven to be an intricate challenge due to fastillumination changes, pupil occlusion, non-centered and off-axis eye recording,as well as physiological eye characteristics. In this paper, we approach thischallenge through: I) a convolutional neural network (CNN) running in real timeon a single core, II) a novel computational intensive two stage CNN foraccuracy improvement, and III) a fast propability distribution based refinementmethod as a practical alternative to II. We evaluate the proposed approachesagainst the state-of-the-art pupil detection algorithms, improving thedetection rate up to ~9% percent points on average over all data sets (~7% onone CPU core 7ms). This evaluation was performed on over 135,000 images: 94,000images from the literature, and 41,000 new hand-labeled and challenging imagescontributed by this work (v1.0).

PupilNet v2.0: Convolutional Neural Networks for CPU based real time Robust Pupil Detection