An efficient hybrid eye detection method

Authors

MINGXIN YU
YINGZI LIN
XIANGZHOU WANG

DOI

10.3906/elk-1312-150

Abstract

Eye detection is the most important and critical task of diverse applications such as face detection and recognition. However, most eye detection methods do not fully consider detection robustness to people with glasses, illumination variation, head pose change, and eye occlusions. This paper proposes an efficient hybrid eye detection method based on a gray intensity variance filter (VF) and support vector machines (SVMs). Firstly, the VF is used for eliminating most of noneye region images to keep less candidate eye regions. Then accurate two eye regions are determined easily through the trained SVM classifier. Moreover, this paper provides an assessment of the sensitivity of obtained parameters in the SVM classifier on eye detection accuracy. The proposed method was evaluated on different face databases. The experimental results show that the method can improve the performance of eye detection and achieve a higher detection accuracy compared with state-of-the-art methods.

Keywords

VF, SVM, eye detection

First Page

1586

Last Page

1603

Recommended Citation

YU, MINGXIN; LIN, YINGZI; and WANG, XIANGZHOU (2016) "An efficient hybrid eye detection method," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 24: No. 3, Article 64. https://doi.org/10.3906/elk-1312-150
Available at: https://journals.tubitak.gov.tr/elektrik/vol24/iss3/64