An Efficient Robust Eye Localization by Learning the Convolution Distribution Using Eye Template.
Eye localization is a fundamental process in many facial analyses. In practical use, it is often challenged by illumination, head pose, facial expression, occlusion, and other factors. It remains great difficulty to achieve high accuracy with short prediction time and low training cost at the same time. This paper presents a novel eye localization approach which explores only one-layer convolution map by eye template using a BP network. Results showed that the proposed method is robust to handle many difficult situations. In experiments, accuracy of 98% and 96%, respectively, on the BioID and LFPW test sets could be achieved in 10 fps prediction rate with only 15-minute training cost. In comparison with other robust models, the proposed method could obtain similar best results with greatly reduced training time and high prediction speed.
Science and Technology on Parallel and Distributed Processing Laboratory, School of Computer, National University of Defense Technology, Changsha 410073, China.
Xuan
X
Li
Science and Technology on Parallel and Distributed Processing Laboratory, School of Computer, National University of Defense Technology, Changsha 410073, China.
Yong
Y
Dou
Science and Technology on Parallel and Distributed Processing Laboratory, School of Computer, National University of Defense Technology, Changsha 410073, China.
Xin
X
Niu
Science and Technology on Parallel and Distributed Processing Laboratory, School of Computer, National University of Defense Technology, Changsha 410073, China.
Jiaqing
J
Xu
Informatization Office, National University of Defense Technology, Changsha 410073, China.
Ruorong
R
Xiao
D000465:Algorithms; D056667:Biometric Identification; D003198:Computer Simulation; D005123:Eye; D005149:Facial Expression; D006801:Humans; D007858:Learning / Q000502:physiology*; D010363:Pattern Recognition, Automated