Iris Center Localization Using Geodesic Distance and CNN

In this paper, we propose a new eye iris center localization method for remote tracking scenarios. The method combines the geodesic distance with CNN-based classification. Firstly, the geodesic distance is used for fast preliminary localization of the regions possibly containing the iris. Then a convolutional neural network is used to carry out the final decision and to refine the final position of the iris center. In the first step, the areas that do not appear to contain the eyeball are quickly filtered out, which makes the whole algorithm fast even on less powerful computers. The proposed method is evaluated and compared with the state-of-the-art methods on two publicly available datasets focused to the remote tracking scenarios (namely BioID [9] , GI4E [15]).