A robust eye detection method based on multi-neighborhood block with weight and SVM on gray image

Liming Yan; Xinzhu Sang

doi:10.1117/12.2033168

19 December 2013 A robust eye detection method based on multi-neighborhood block with weight and SVM on gray image

Liming Yan, Xinzhu Sang

Proceedings Volume 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology; 90450Y (2013) https://doi.org/10.1117/12.2033168
Event: International Conference on Optical Instruments and Technology (OIT2013), 2013, Beijing, China

Abstract

Eye detection plays a vital role in intelligent recognition system. In this paper, a novel of robust and fast eye detection algorithm is proposed. The process of eye detection can be divided into two stages. Firstly, coarse eye candidates are extracted based on multi-neighborhood blocks with weight; Secondly, eye candidates validation is testified on support vector machine (SVM).Data from different face databases such as self-built, JAFFE, FERET are used to demonstrate the effectiveness and robustness of the proposed method.

Citation Download Citation

Liming Yan and Xinzhu Sang "A robust eye detection method based on multi-neighborhood block with weight and SVM on gray image", Proc. SPIE 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 90450Y (19 December 2013); https://doi.org/10.1117/12.2033168

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available