论文标题
用于建模人机交互的智能手机传感器：用于用户认证的一般前景和研究数据集
Smartphone Sensors for Modeling Human-Computer Interaction: General Outlook and Research Datasets for User Authentication
论文作者
论文摘要
在本文中，我们列出了现代智能手机中常用的传感器，并提供了这些传感器可以使用不同方式进行建模人与智能手机之间的相互作用的一般前景。然后，我们提供了应用程序的分类法，可以利用这些传感器在三个不同维度上发起的信号，具体取决于应用程序中利用的信号中嵌入的主要信息内容：神经运动技能，认知功能和行为/行为/例程。然后，我们总结了该领域中现有研究数据集的代表性选择，特别关注与用户身份验证有关的应用程序，包括关键功能和迄今为止获得的主要研究结果的选择。然后，我们使用HUMIDB数据库（人类移动交互数据库）进行实验工作，这是一个新型的多模式移动数据库，其中包括600名参与者捕获的14个移动传感器。我们使用暹罗神经网络体系结构评估基于简单的线性触摸手势的生物识别身份验证系统。基于简单而快速的触摸手势的人身份验证，精度可实现非常有希望的结果。
In this paper we list the sensors commonly available in modern smartphones and provide a general outlook of the different ways these sensors can be used for modeling the interaction between human and smartphones. We then provide a taxonomy of applications that can exploit the signals originated by these sensors in three different dimensions, depending on the main information content embedded in the signals exploited in the application: neuromotor skills, cognitive functions, and behaviors/routines. We then summarize a representative selection of existing research datasets in this area, with special focus on applications related to user authentication, including key features and a selection of the main research results obtained on them so far. Then, we perform the experimental work using the HuMIdb database (Human Mobile Interaction database), a novel multimodal mobile database that includes 14 mobile sensors captured from 600 participants. We evaluate a biometric authentication system based on simple linear touch gestures using a Siamese Neural Network architecture. Very promising results are achieved with accuracies up to 87% for person authentication based on a simple and fast touch gesture.
