Tiny convolution, decision tree, and binary neuronal networks for robust and real time pupil outline estimation
Wolfgang Fuhl, Hong Gao, Enkelejda Kasneci
Abstract
In this work, we compare the use of convolution, binary, and decision tree layers in neural networks for the estimation of pupil landmarks. These landmarks are used for the computation of the pupil ellipse and have proven to be effective in previous research. The evaluated structure of the neural networks is the same for all layers and as small as possible to ensure a real-time application. The evaluations include the accuracy of the ellipse determination based on the Jaccard Index and the pupil center. Furthermore, the CPU runtime is considered to make statements about the real-time usability. The trained models are also optimized using pruning to improve the runtime. These optimized nets are also evaluated with respect to the Jaccard index and the accuracy of the pupil center estimation. Link to the framework and models.