Deconvolutional Neural Network for Pupil Detection in Real-World Environments

被引：13

作者：

Vera-Olmos, F. J. ^{[1
]}

Malpica, N. ^{[1
]}

机构：

[1] Univ Rey Juan Carlos, Madrid, Spain

来源：

BIOMEDICAL APPLICATIONS BASED ON NATURAL AND ARTIFICIAL COMPUTING, PT II | 2017年 / 10338卷

关键词：

D O I：

10.1007/978-3-319-59773-7_23

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Eyelid identification provides key data that can be used in several application such as controlling gaze-based HMIs (human machine interfaces), the design of new diagnostic tools for brain diseases, improving driver safety, drowsiness detection, research on advertisement, etc. We propose a novel eyetracking algorithm by learning a deep deconvolutional neural network. To train and test our method, we use several data sets with hand-labeled eye images from real-world tasks. Our method outperforms previous eye tracking methods, improving the results of the current state of the art in a 19%.

引用

页码：223 / 231

页数：9

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