%0 Journal Article
%T Robust Eye Localization by Combining Classification and Regression Methods
%A Pak Il Nam
%A Ri Song Jin
%A Peter Peer
%J ISRN Applied Mathematics
%D 2014
%R 10.1155/2014/804291
%X 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. 1. Introduction Because face images should be normalized based on the coordinates of eyes in most face recognition systems, eye localization is an important part in face recognition systems. Its precision closely affects the performance of face recognition [1, 2]. Eye localization methods considering geometric properties of eyes such as edges, shape, and probabilistic characteristics are high in precision in normal conditions, but they are sensitive to illumination, pose, expression, and glasses [3¨C6]. State-of-the-art methods in eye localization are based on boosting classification, regression, boosting and cascade, boosting and SVM, and other variants [1, 2, 7¨C11]. In particular, the method in [1] is very effective, guaranteeing high precision even in unconstrained environment. It integrates the following three characteristics:(i)probabilistic cascade,(ii)two-level localization framework,(iii)extended local binary pattern (ELBP). In eye localization, the boundary between the positive and negative samples is ambiguous, especially in low quality images. Thus, positive samples with low quality are easily rejected by the thresholds in the cascade and fail to contribute to the final result. In [1] the authors introduced a quality adaptive cascade that works in a probabilistic framework (P cascade). In the P cascade framework all image patches have a chance to contribute to the final result and their contributions are determined by their corresponding probability. In this way P cascade can adapt to face images of arbitrary quality. Furthermore, they constructed two-level localization framework with a coarse-to-fine localization for the system to be robust and accurate. Figure 1 shows the size and geometry of the eye training samples for two-level stacked classifiers. Figure 1: The size and geometry of training samples for two-level stacked classifiers. In order to
%U http://www.hindawi.com/journals/isrn.applied.mathematics/2014/804291/