Name
Abstract | ||
|---|---|---|
Eye center localization has been an active research topic for decades due to its important biological properties, which indicates humanu0027s 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 |
Year | Venue | Field |
|---|---|---|
2018 | BMVC | Computer vision,Pattern recognition,Convolution,Computer science,Sclera,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haibin Cai | 1 | 38 | 6.46 |
| Bangli Liu | 2 | 11 | 2.85 |
| Zhaojie Ju | 3 | 284 | 48.23 |
| S. Thill | 4 | 91 | 14.16 |
| Tony Belpaeme | 5 | 13 | 3.98 |
| Bram Vanderborght | 6 | 1029 | 117.65 |
| Honghai Liu | 7 | 1974 | 178.69 |