An Approach to Biometric Verification Based on Human Body Communication in Wearable Devices.
An Approach to Biometric Verification Based on Human Body Communication in Wearable Devices.
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可穿戴设备中基于人体通信的生物特征验证方法
DOI：
10.3390/s17010125
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发表时间：
2017-01-10
期刊：
影响因子：
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通讯作者：
Wang L
中科院分区：
文献类型：
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作者：
Li J;Liu Y;Nie Z;Qin W;Pang Z;Wang L
提出了一种基于人体通信的可穿戴设备生物特征验证方法。为此，通过矢量网络分析仪（VNA）测量志愿者前臂的传输增益S21。具体地，为了确定用于生物特征验证的所选频率，在0.3MHz至1500 MHz的频率范围内从10名志愿者获取1800组数据，并且每组包括1601个样本数据。此外，为了实现快速验证，在选定的频率下采集每个志愿者的30组数据，每组仅包含21个样本数据。在此基础上，提出了一种基于加权欧氏距离的阈值自适应模板匹配（TATM）算法。结果表明，生物特征验证的选择频率为650 MHz至750 MHz。基于TATM的错误接受率（FAR）和错误拒绝率（FRR）分别约为5.79%和6.74%。相比之下，FAR和FRR分别为4.17%和37.5%，3.37%和33.33%，3.80%和34.17%，使用K-最近邻（KNN）分类，支持向量机（SVM）和朴素贝叶斯方法（NBM）分类。此外，TATM的运行时间为0.019 s，而KNN，SVM和NBM的运行时间分别为0.310 s，0.0385 s和0.168 s。因此，建议TATM适用于可穿戴设备中的快速验证。
In this paper, an approach to biometric verification based on human body communication (HBC) is presented for wearable devices. For this purpose, the transmission gain S21 of volunteer’s forearm is measured by vector network analyzer (VNA). Specifically, in order to determine the chosen frequency for biometric verification, 1800 groups of data are acquired from 10 volunteers in the frequency range 0.3 MHz to 1500 MHz, and each group includes 1601 sample data. In addition, to achieve the rapid verification, 30 groups of data for each volunteer are acquired at the chosen frequency, and each group contains only 21 sample data. Furthermore, a threshold-adaptive template matching (TATM) algorithm based on weighted Euclidean distance is proposed for rapid verification in this work. The results indicate that the chosen frequency for biometric verification is from 650 MHz to 750 MHz. The false acceptance rate (FAR) and false rejection rate (FRR) based on TATM are approximately 5.79% and 6.74%, respectively. In contrast, the FAR and FRR were 4.17% and 37.5%, 3.37% and 33.33%, and 3.80% and 34.17% using K-nearest neighbor (KNN) classification, support vector machines (SVM), and naive Bayesian method (NBM) classification, respectively. In addition, the running time of TATM is 0.019 s, whereas the running times of KNN, SVM and NBM are 0.310 s, 0.0385 s, and 0.168 s, respectively. Therefore, TATM is suggested to be appropriate for rapid verification use in wearable devices.