  We propose a new cascaded regressor for eye center detection. Previous
methods start from a face or an eye detector and use either advanced features
or powerful regressors for eye center localization, but not both. Instead, we
detect the eyes more accurately using an existing facial feature alignment
method. We improve the robustness of localization by using both advanced
features and powerful regression machinery. Unlike most other methods that do
not refine the regression results, we make the localization more accurate by
adding a robust circle fitting post-processing step. Finally, using a simple
hand-crafted method for eye center localization, we show how to train the
cascaded regressor without the need for manually annotated training data. We
evaluate our new approach and show that it achieves state-of-the-art
performance on the BioID, GI4E, and the TalkingFace datasets. At an average
normalized error of e < 0.05, the regressor trained on manually annotated data
yields an accuracy of 95.07% (BioID), 99.27% (GI4E), and 95.68% (TalkingFace).
The automatically trained regressor is nearly as good, yielding an accuracy of
93.9% (BioID), 99.27% (GI4E), and 95.46% (TalkingFace).
