Abstract
We present a new method for eye pupil detection in images. The algorithm runs in two steps. Firstly, a reasonable number of good candidates for pupil position are determined quickly by making use of the self-organizing migrating algorithm. Subsequently, the final position of pupil, among the preselected candidates, is determined precisely by making use of a convolutional neural network. The motivation for this two-step architecture is to create the algorithm that is both precise and fast. The favorable computational speed follows from the fact that only the meaningful positions and sizes are checked in the potentially most time-consuming second step. Moreover, the demands on training and the training set for the network are lower than if the network is used exclusively in one step architecture. The algorithm is capable to run on less powerful computers, e.g. on embedded computers in cars. In our tests, the algorithm achieved good results.
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This work was partially supported by Grant of SGS No. SP2019/71, VŠB - Technical University of Ostrava, Czech Republic.
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Fusek, R., Sojka, E., Holusa, M. (2019). Pupil Center Localization Using SOMA and CNN. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_34
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