BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation
BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation
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DOI：
10.1016/j.patcog.2022.109089
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发表时间：
2022-10-17
影响因子：
8
通讯作者：
Morales, Aythami
中科院分区：
文献类型：
--
作者：
Stragapede, Giuseppe;Vera-Rodriguez, Ruben;Morales, Aythami
移动的行为生物识别技术已经成为一个热门的研究课题，在身份验证方面取得了可喜的成果，利用触摸屏和背景传感器数据的多模式组合。但是，无法知道文献中最先进的分类器是否可以区分用户和器械的概念。在这篇文章中，我们提出了一个新的数据库，BehavePassDB，结构成单独的采集会话和任务，以模仿最常见的方面的移动的人机交互（HCI）。BehavePassDB是通过安装在受试者设备上的专用移动的应用程序获取的，也包括不同用户在同一设备上进行评估的情况。我们提出了一个标准的实验协议和基准的研究社区执行一个公平的比较新的方法与最先进的1。我们提出并评估了一个基于长短期记忆（LSTM）架构的系统，该系统具有三重丢失和模态融合。(c)2022作者（S）爱思唯尔有限公司出版
Mobile behavioral biometrics have become a popular topic of research, reaching promising results in terms of authentication, exploiting a multimodal combination of touchscreen and background sensor data. However, there is no way of knowing whether state-of-the-art classifiers in the literature can distinguish between the notion of user and device. In this article, we present a new database, BehavePassDB, structured into separate acquisition sessions and tasks to mimic the most common aspects of mobile Human-Computer Interaction (HCI). BehavePassDB is acquired through a dedicated mobile app installed on the subjects devices, also including the case of different users on the same device for evaluation. We propose a standard experimental protocol and benchmark for the research community to perform a fair comparison of novel approaches with the state of the art1. We propose and evaluate a system based on Long-Short Term Memory (LSTM) architecture with triplet loss and modality fusion at score level. (c) 2022 The Author(s). Published by Elsevier Ltd.