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arXiv 2201.02110cs.CVcs.HC

Eye Know You Too：一种用于端到端眼动生物识别的 DenseNet 架构

Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics

Texas State University（德克萨斯州立大学）

Dillon Lohr, Oleg V Komogortsev

备注：
This work has been submitted to the IEEE for possible publication
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AI总结：

本文提出一种用于端到端眼动生物识别的 DenseNet 架构，并通过与相关既往方法比较，证明其超越现有最先进性能且首次接近实际可用的认证水平。

摘要：

眼动生物识别（EMB）是一种相对较新的行为生物识别模态；由于虚拟现实和增强现实设备正越来越多地使用眼动追踪传感器以实现注视点渲染技术，EMB 有可能成为这些设备中的主要身份认证方式。然而，现有 EMB 模型尚未展示出可满足现实使用要求的性能水平。面向 EMB 的深度学习方法大多采用普通卷积神经网络（CNN），但多年来卷积架构已经出现了许多里程碑式的改进，包括残差网络（ResNet）和密集连接卷积网络（DenseNet）。本研究采用 DenseNet 架构进行端到端 EMB，并将所提出的模型与最相关的既往工作进行比较。所提出的技术不仅优于此前的最先进方法，而且首次接近了可被现实使用接受的身份认证性能水平。

英文摘要：

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.

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。

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