Learning multi-channel correlation filter bank for eye localization

Accurate eye localization plays a key role in many face analysis related applications. In this paper, we propose a novel statistic-based eye localization framework with a group of trained filter arrays called multi-channel correlation filter bank (MCCFB). Each filter array in the bank suits to a different face condition, thus combining these filter arrays can locate eyes more precisely in the conditions of variable poses, appearances and illuminations when comparing to single filter based or filter array based methods. To demonstrate the performance of our framework, we compare MCCFB with other statistic-based eye localization methods, experimental results show superiority of our method in detection ratio, localization accuracy and robustness.