TY - JOUR
T1 - An efficient hybrid eye detection method
AU - Yu, Mingxin
AU - Lin, Yingzi
AU - Wang, Xiangzhou
N1 - Publisher Copyright:
© TÜBİTAK.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Eye detection
KW - SVM
KW - VF
UR - http://www.scopus.com/inward/record.url?scp=84963901423&partnerID=8YFLogxK
U2 - 10.3906/elk-1312-150
DO - 10.3906/elk-1312-150
M3 - Article
AN - SCOPUS:84963901423
SN - 1300-0632
VL - 24
SP - 1586
EP - 1603
JO - Turkish Journal of Electrical Engineering and Computer Sciences
JF - Turkish Journal of Electrical Engineering and Computer Sciences
IS - 3
ER -