Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel.
Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel.
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DOI：
10.3390/s17102228
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发表时间：
2017-09-28
期刊：
影响因子：
--
通讯作者：
Carreiras C
中科院分区：
文献类型：
--
作者：
Pinto JR;Cardoso JS;Lourenço A;Carreiras C
通过方向盘获取的心电信号可能是在驾驶环境中无缝、高度舒适和连续识别人类的关键。通过结合Savitzky-Golay和移动平均滤波器，然后基于归一化互相关和聚类的离群点检测和去除，能够提供明显更高质量的组合心跳，从而提高这类信号的质量。提取离散余弦变换(DCT)和Haar变换特征，并将其提供给基于支持向量机(SVM)、k近邻(KNN)、多层感知器(MLP)和高斯混合模型-通用背景模型(GMM-UBM)分类器的决策方法，用于识别和认证任务。还研究了用户调整的身份验证和过去分数加权的其他技术。该方法的性能与最近一些最先进的方法(94.9%的识别率(IDR)和2.66%的认证等错误率(EER))相当，尽管在训练数据稀少的情况下结果较差(70.9%的IDR和11.8%的EER)。结果表明，该方法适用于驾驶心电信号的生物识别，并可在无缝、高噪声环境下的连续系统上应用。
Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method’s performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings.
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影响因子：
2
作者：
Coutinho, David Pereira;Silva, Hugo;Figueiredo, Mario
通讯作者：
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影响因子：
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作者：
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DOI：
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发表时间：
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影响因子：
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作者：
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影响因子：
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作者：
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