Paper Info
Title
Sdm-Based Means Of Gradient For Eye Center Localization
Abstract
For eye gaze estimation and eye tracking, localizing eye center is a crucial requirement. This task is challenging work because of the significant variability of eye appearance in illumination, shape, color and viewing angles. In this paper, we improve the performance of means of gradient method in low resolution images, which could locate the eye center more accurately. The proposed method applies Supervised Descent Method (SDM), which has remarkable achievement in the field of face alignment, to improve the traditional means of gradient method in localizing eye center. We extensively evaluate our method on BioID database which is very challenging and realistic for eye center localization. Moreover, we have compared our method with existing state of the art methods and the results of the experiment confirm that the proposed method is an attractive alternative for eye center localization.
Year
DOI
Venue
2018
10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00-17
2018 16TH IEEE INT CONF ON DEPENDABLE, AUTONOM AND SECURE COMP, 16TH IEEE INT CONF ON PERVAS INTELLIGENCE AND COMP, 4TH IEEE INT CONF ON BIG DATA INTELLIGENCE AND COMP, 3RD IEEE CYBER SCI AND TECHNOL CONGRESS (DASC/PICOM/DATACOM/CYBERSCITECH)
Keywords
Field
DocType
Eye Gaze Estimation, Eye Center Localization, Means of Gradient
Gradient method,Computer vision,Supervised descent method,Computer science,Eye tracking,Artificial intelligence
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Yifan Xia1152.50
Jianwen Lou261.45
Junyu Dong339377.68
Gongfa Li423943.45
Hui Yu5153.75