基于长短期记忆模型的用户身份识别过程

Bengie L. Ortiz, Vibhuti Gupta, J. Chong, Kwanghee Jung, Tim Dallas
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引用次数: 0

摘要

用户身份验证(UA)是一个人使用生物识别技术来访问物理或虚拟站点的过程。UA已经在各种应用中实现,例如金融交易、数据隐私和访问控制。各种技术,如面部和指纹识别,已被提出用于医疗保健监测,以解决生物识别问题。光体积脉搏描记(PPG)技术是一种光学传感技术,它从受试者指尖、耳垂或前额附近的皮肤收集体积血液变化数据。PPG信号可以很容易地从智能手机、智能手表或网络摄像头等设备获取。经典的机器学习技术,如决策树、支持向量机(SVM)和k近邻(kNN),已被提出用于PPG识别。我们开发了一种基于长短期记忆(LSTM)的智能设备UA分类方法。具体来说,我们的UA分类器算法使用原始信号,这样就不会失去来自每个用户特定行为的PPG信号的特定特征。在UA上下文中,假阳性和假阴性率是至关重要的。我们招募了30名健康受试者,并使用智能手机采集PPG数据。实验结果表明,基于bi - lstm的UA算法基于特征的机器学习和基于原始数据的深度学习方法,准确率分别为95.0%和96.7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
User Authentication Recognition Process Using Long Short-Term Memory Model
User authentication (UA) is the process by which biometric techniques are used by a person to gain access to a physical or virtual site. UA has been implemented in various applications such as financial transactions, data privacy, and access control. Various techniques, such as facial and fingerprint recognition, have been proposed for healthcare monitoring to address biometric recognition problems. Photoplethysmography (PPG) technology is an optical sensing technique which collects volumetric blood change data from the subject’s skin near the fingertips, earlobes, or forehead. PPG signals can be readily acquired from devices such as smartphones, smartwatches, or web cameras. Classical machine learning techniques, such as decision trees, support vector machine (SVM), and k-nearest neighbor (kNN), have been proposed for PPG identification. We developed a UA classification method for smart devices using long short-term memory (LSTM). Specifically, our UA classifier algorithm uses raw signals so as not to lose the specific characteristics of the PPG signal coming from each user’s specific behavior. In the UA context, false positive and false negative rates are crucial. We recruited thirty healthy subjects and used a smartphone to take PPG data. Experimental results show that our Bi-LSTM-based UA algorithm based on the feature-based machine learning and raw data-based deep learning approaches provides 95.0% and 96.7% accuracy, respectively.
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