Robust Eye Localization by Combining Classification and Regression Methods

Abstract

Eye localization is an important part in face recognition system, because its precision closely affects the performance of the system. In this paper we analyze the limitations of classification and regression methods and propose a robust and accurate eye localization method combining these two methods. The classification method in eye localization is robust, but its precision is not so high, while the regression method is sensitive to the initial position, but in case the initial position is near to the eye position, it can converge to the eye position accurately. Experiments on BioID and LFW databases show that the proposed method gives very good results on both low and high quality images.