基于Capsule网络的眼周NIR图像酒精摄入量检测
Alcohol Consumption Detection from Periocular NIR Images Using Capsule Network
摘要：
本研究提出一种从近红外（NIR）眼周图像中检测饮酒量的方法。这项研究的重点是确定外部因素，如酒精对中枢神经系统（CNS）的影响。我们的目标是分析这对虹膜和瞳孔运动的影响，以及是否有可能用标准虹膜近红外相机捕捉这些变化。本文提出了一种新的融合胶囊网络（F-CapsNet）的虹膜近红外图像分类下饮酒的主题。实验结果表明，F-CapsNet算法可以检测虹膜近红外图像中的酒精消耗，准确率为92.3%，使用的参数为标准胶囊网络算法的一半。这项工作是在开发一个自动系统，以评估“健康的职责”和防止事故，由于酒精消费向前迈进了一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
