论文标题
使用胶囊网络从眼周NIR图像中检测饮酒
Alcohol Consumption Detection from Periocular NIR Images Using Capsule Network
论文作者
论文摘要
这项研究提出了一种检测近距离（NIR）眼周眼图像的酒精消耗的方法。该研究重点是确定外部因素（例如酒精对中枢神经系统（CNS））的影响。目的是分析这如何影响虹膜和学生运动，以及是否有可能使用标准的Iris NIR相机捕获这些更改。本文提出了一个新型的融合胶囊网络（F-CAPSNET），以对饮酒受试者拍摄的虹膜NIR图像进行分类。结果表明，使用一半参数作为标准胶囊网络算法，F-CAPSNET算法可以在IRIS NIR图像中检测出液化液的消耗，准确度为92.3％。这项工作是开发自动系统以估算“适合值班”并防止因饮酒而导致事故的一步。
This research proposes a method to detect alcohol consumption from Near-Infra-Red (NIR) periocular eye images. The study focuses on determining the effect of external factors such as alcohol on the Central Nervous System (CNS). The goal is to analyse how this impacts on iris and pupil movements and if it is possible to capture these changes with a standard iris NIR camera. This paper proposes a novel Fused Capsule Network (F-CapsNet) to classify iris NIR images taken under alcohol consumption subjects. The results show the F-CapsNet algorithm can detect alcohol consumption in iris NIR images with an accuracy of 92.3% using half of the parameters as the standard Capsule Network algorithm. This work is a step forward in developing an automatic system to estimate "Fitness for Duty" and prevent accidents due to alcohol consumption.
