论文标题
眼睛也知道您：端到端眼动生物识别的登克语体系结构
Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics
论文作者
论文摘要
眼动生物识别（EMB）是一种相对较新的行为生物识别模式，由于其新兴使用眼轨传感器的新现实设备可以成为虚拟和增强设备中的主要身份验证方法，从而启用了Foveated渲染技术。但是，现有的EMB模型尚未证明可用于现实世界使用的性能水平。深度学习方法的EMB在很大程度上采用了普通的卷积神经网络（CNN），但是多年来，卷积架构的里程碑改进了许多里程碑，包括残留网络（RESNETS）（RESNETS）和密度连接的卷积网络（Densenets）。本研究采用了端到端EMB的Densenet架构，并将提议的模型与最相关的先前工作进行了比较。提出的技术不仅胜过先前的艺术状态，而且还是第一个达到现实使用的身份验证性能水平的水平。
Eye movement biometrics (EMB) is a relatively recent behavioral biometric modality that may have the potential to become the primary authentication method in virtual- and augmented-reality devices due to their emerging use of eye-tracking sensors to enable foveated rendering techniques. However, existing EMB models have yet to demonstrate levels of performance that would be acceptable for real-world use. Deep learning approaches to EMB have largely employed plain convolutional neural networks (CNNs), but there have been many milestone improvements to convolutional architectures over the years including residual networks (ResNets) and densely connected convolutional networks (DenseNets). The present study employs a DenseNet architecture for end-to-end EMB and compares the proposed model against the most relevant prior works. The proposed technique not only outperforms the previous state of the art, but is also the first to approach a level of authentication performance that would be acceptable for real-world use.
