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).
@cite_27: 
@cite_18: We describe a method for eye pupil localization based on an ensemble of randomized regression trees and use several publicly available datasets for its quantitative and qualitative evaluation. The method compares well with reported state-of-the-art and runs in real-time on hardware with limited processing power, such as mobile devices. HighlightsA framework for eye pupil localization that compares well with state-of-the-art.Randomization during runtime improves performance.The developed system works in real-time on mobile devices.
@cite_8: Eye pupil localization is an important part in computer vision applications such as face recognition, gaze estimation and so on. In this paper, we propose an improved method for precise and fast eye pupil localization. Based on gradient boosting decision tree(GBDT) algorithm, a more accurate localization is achieved by increasing the weight of the training samples with larger errors in a moderate rate. Furthermore, a pruning strategy is utilized to avoid overfitting and reduce the localization time without accuracy loss. Experimental results show that the improved method achieves an accuracy of 92.39 at a speed as fast as 1.7ms to locate in the range of eye pupil on BioID database. The proposed method outperforms most state-of-the-art methods in terms of localization accuracy and consumed time.
While the above methods are accurate, they still lack robustness in challenging in-the-wild scenarios. The success of discriminative cascaded regression for facial feature alignment prompted the use of such methods for eye center localization. @cite_18 @cite_8 start by detecting the face and initializing the eye center estimates using anthropometric relations. Subsequently, they use a cascade of regression forests with binary pixel difference features to estimate the eye centers. Inspired by the recent success of the SDM method for facial feature alignment Zhou al @cite_27 propose a similar method for eye center localization. Unlike the original SDM work, their regressor is based on a combination of SIFT and LBP features. Moreover, unlike @cite_18 @cite_8 who regress individual eye centers, Zhou al estimate a shape vector that includes both eye centers and eye contours. In line with this trend we develop a new regression-based eye center estimator, but additionally employ circle-based refinement and voting-based techniques to get an accurate detector that is easy to train.