Table of Contents
ISRN Applied Mathematics
Volume 2014 (2014), Article ID 804291, 7 pages
http://dx.doi.org/10.1155/2014/804291
Research Article

Robust Eye Localization by Combining Classification and Regression Methods

1Institute of Mathematics, State Academy of Sciences, Democratic People’s Republic of Korea
2Faculty of Computer and Information Science, University of Ljubljana, Slovenia

Received 24 November 2013; Accepted 4 March 2014; Published 30 March 2014

Academic Editors: Y. Dimakopoulos and Z. Huang

Copyright © 2014 Pak Il Nam et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.