important
biological properties, which indicates human’s visual focus of attention. However,
accurate eye center localization still remains challenging due to the high degree appearance
variation caused by different kinds of viewing angles, illumination conditions, occlusions
and head pose. This paper proposes a hierarchical adaptive convolution method
(HAC) to localize the eye center accurately while consuming low computational cost.
It mainly utilizes the dramatic illumination changes between the iris and sclera. More
specifically, novel hierarchical kernels are designed to convolute the eye images and a
differential operation is applied on the adjacent convolution results to generate various response
maps. The final eye center is localized by searching the maximum response value
among the response maps. Experimental results on several publicly available datasets
demonstrate that HAC outperforms the start-of-the-art methods by a large margin. The
code is made publicly available at https://github.com/myopengit/HAC
Original languageEnglish
Title of host publication British Machine Vision Conference (BMVC)
Publication statusAccepted/In press - 15 Jul 2018

ID: 38818720