Robust Eye Localization by Combining Classification and Regression Methods

Pak Il Nam,Ri Song Jin,Peter Peer

doi:10.1155/2014/804291

Pak Il Nam, Ri Song Jin + Show 1 more

Open Access

https://doi.org/10.1155/2014/804291

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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.

Highlights

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
In this paper we proposed an eye localization method of high precision by the combination of classification and regression methods
The proposed method is based on facts that classification method is robust but less accurate, while regression method is less robust but very accurate because it has more information about object position

Summary

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. 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. They constructed two-level localization framework with a coarse-to-fine localization for the system to be robust and accurate. In this paper we propose an eye localization method with two-level localization framework, which is both robust and accurate even in unconstrained environment.

Proposed Eye Localization Method

Experimental Results

Conclusion