An accurate eye pupil localization approach based on adaptive gradient boosting decision tree
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.