The existing one-time identity authentication technology cannot continuously guarantee the legitimacy of user identity during the whole human-computer interaction session, and often requires active cooperation of users, which seriously limits the availability. This study proposes a new non-contact identity recognition technology based on cardiac micro-motion detection using ultra wideband (UWB) bio-radar. After the multi-point micro-motion echoes in the range dimension of the human heart surface area were continuously detected by ultra wideband bio-radar, the two-dimensional principal component analysis (2D-PCA) was exploited to extract the compressed features of the two-dimensional image matrix, namely the distance channel-heart beat sampling point (DC-HBP) matrix, in each accurate segmented heart beat cycle for identity recognition. In the practical measurement experiment, based on the proposed multi-range-bin & 2D-PCA feature scheme along with two conventional reference feature schemes, three typical classifiers were selected as representatives to conduct the heart beat identification under two states of normal breathing and breath holding. The results showed that the multi-range-bin & 2D-PCA feature scheme proposed in this paper showed the best recognition effect. Compared with the optimal range-bin & overall heart beat feature scheme, our proposed scheme held an overall average recognition accuracy of 6.16% higher (normal respiration: 6.84%; breath holding: 5.48%). Compared with the multi-distance unit & whole heart beat feature scheme, the overall average accuracy increase was 27.42% (normal respiration: 28.63%; breath holding: 26.21%) for our proposed scheme. This study is expected to provide a new method of undisturbed, all-weather, non-contact and continuous identification for authentication.
现有一次性身份认证技术无法持续保证整个人-机交互会话过程中的用户身份合法性，且往往需要用户主动配合而严重限制可用性。本研究首次提出一种基于超宽谱（UWB）生物雷达检测心脏微动的非接触身份识别新技术，通过生物雷达连续检测心脏体表区域距离维多点微动回波，在心拍分割的基础上利用二维主成分分析（2D-PCA）压缩提取心拍周期内距离通道-采样点二维图像的矩阵特征用于身份识别。实测实验中，以多距离单元& 2D-PCA特征方案为基础结合两种常规的参考特征方案，选取三种典型分类器为代表在正常呼吸和屏息两种状态下进行心拍身份识别。结果表明，本文所提多距离单元& 2D-PCA特征方案表现出最优的识别效果（识别率最高可达90%以上），相对最佳距离单元&整条心拍特征方案识别准确率总体平均提高6.16%（正常呼吸6.84%、屏息5.48%），相对多距离单元&整条心拍特征方案总体平均提高27.42%（正常呼吸28.63%、屏息26.21%）。本研究有望为未来社会用户信息安全防护提供一种无扰式、全天候、非接触、连续性身份识别新方法。.
Keywords: Bio-radar; Heart micro-motion; Human identification; Microwave heartprint; Non-contact.