Deep Learning Pupil Center Localization

Abstract: This project strives to achieve high performance object localization with Convolutional Neural Networks (CNNs) - in particular for pupil centers in the context of remote eye tracking systems. Three different network architectures suitable to the task are developed, evaluated and compared - one based on regression using fully connected layers, one Fully Convolutional Network and one Deconvolutional Network. The best performing model achieves a mean error of only 0.52 pixel distance and a median error of 0.42 pixel distance compared to the ground truth annotations. The 95th percentile lies at 1.12 pixel error. This exceeds the performance of current state-of-the-art pupil center detection algorithms by an order of magnitude, a result that can be accredited both to the algorithm as well as to the dataset which exceeds datasets used for this purpose in prior publications in suitability, quality and size. Opportunities for further improvements of the computational cost based on recent model compression research are suggested.