Standard

Accurate Eye Center Localization via Hierarchical Adaptive Convolution. / Cai, Haibin; Liu, Bangli; Ju, Zhaojie; Thill, Serge; Belpaeme, Tony; Vanderborght, Bram; Liu, Honghai.

British Machine Vision Conference (BMVC). 2018.

Research output: Chapter in Book/Report/Conference proceedingConference paperResearch

Harvard

Cai, H, Liu, B, Ju, Z, Thill, S, Belpaeme, T, Vanderborght, B & Liu, H 2018, Accurate Eye Center Localization via Hierarchical Adaptive Convolution. in British Machine Vision Conference (BMVC).

APA

Cai, H., Liu, B., Ju, Z., Thill, S., Belpaeme, T., Vanderborght, B., & Liu, H. (Accepted/In press). Accurate Eye Center Localization via Hierarchical Adaptive Convolution. In British Machine Vision Conference (BMVC)

Vancouver

Cai H, Liu B, Ju Z, Thill S, Belpaeme T, Vanderborght B et al. Accurate Eye Center Localization via Hierarchical Adaptive Convolution. In British Machine Vision Conference (BMVC). 2018

Author

Cai, Haibin ; Liu, Bangli ; Ju, Zhaojie ; Thill, Serge ; Belpaeme, Tony ; Vanderborght, Bram ; Liu, Honghai. / Accurate Eye Center Localization via Hierarchical Adaptive Convolution. British Machine Vision Conference (BMVC). 2018.

BibTeX

@inproceedings{47f0051d46484df7a91c46cab77b957c,
title = "Accurate Eye Center Localization via Hierarchical Adaptive Convolution",
abstract = "importantbiological properties, which indicates human’s visual focus of attention. However,accurate eye center localization still remains challenging due to the high degree appearancevariation caused by different kinds of viewing angles, illumination conditions, occlusionsand 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. Morespecifically, novel hierarchical kernels are designed to convolute the eye images and adifferential operation is applied on the adjacent convolution results to generate various responsemaps. The final eye center is localized by searching the maximum response valueamong the response maps. Experimental results on several publicly available datasetsdemonstrate that HAC outperforms the start-of-the-art methods by a large margin. Thecode is made publicly available at https://github.com/myopengit/HAC",
author = "Haibin Cai and Bangli Liu and Zhaojie Ju and Serge Thill and Tony Belpaeme and Bram Vanderborght and Honghai Liu",
year = "2018",
month = "7",
day = "15",
language = "English",
booktitle = "British Machine Vision Conference (BMVC)",

}

RIS

TY - GEN

T1 - Accurate Eye Center Localization via Hierarchical Adaptive Convolution

AU - Cai, Haibin

AU - Liu, Bangli

AU - Ju, Zhaojie

AU - Thill, Serge

AU - Belpaeme, Tony

AU - Vanderborght, Bram

AU - Liu, Honghai

PY - 2018/7/15

Y1 - 2018/7/15

N2 - importantbiological properties, which indicates human’s visual focus of attention. However,accurate eye center localization still remains challenging due to the high degree appearancevariation caused by different kinds of viewing angles, illumination conditions, occlusionsand 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. Morespecifically, novel hierarchical kernels are designed to convolute the eye images and adifferential operation is applied on the adjacent convolution results to generate various responsemaps. The final eye center is localized by searching the maximum response valueamong the response maps. Experimental results on several publicly available datasetsdemonstrate that HAC outperforms the start-of-the-art methods by a large margin. Thecode is made publicly available at https://github.com/myopengit/HAC

AB - importantbiological properties, which indicates human’s visual focus of attention. However,accurate eye center localization still remains challenging due to the high degree appearancevariation caused by different kinds of viewing angles, illumination conditions, occlusionsand 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. Morespecifically, novel hierarchical kernels are designed to convolute the eye images and adifferential operation is applied on the adjacent convolution results to generate various responsemaps. The final eye center is localized by searching the maximum response valueamong the response maps. Experimental results on several publicly available datasetsdemonstrate that HAC outperforms the start-of-the-art methods by a large margin. Thecode is made publicly available at https://github.com/myopengit/HAC

M3 - Conference paper

BT - British Machine Vision Conference (BMVC)

ER -

ID: 38818720