An improved eye localization algorithm using multi-cue facial information

In this paper, we propose a four step method to detect center of eyes robustly and accurately. The method doesn´t put any restrictions on the background. The method is based on AdaBoost algorithm for face and eye candidate points detection. Candidate points are tuned such that two candidate points are exactly in centers of irises. Mean crossing function and convolution template are proposed to filter out candidate points and select iris pair. The advantage of using this kind of hybrid method is that AdaBoost is robust to different illumination conditions and backgrounds. The tuning step improves the precision of iris localization while the convolution filter and mean crossing function reliably filter out candidate points and select iris pair. The proposed structure is evaluated on three public databases, Bern, Yale and BioID. Extensive experimental results verified the robustness and accuracy of the proposed method. Using the Bern database, the performance of the proposed algorithm is also compared with some of the existing methods.