Huang Wei, Ren Wei, Wang Kehan, Li Zhao, Wang Jianqi, Lu Guohua, Qi Fugui
School of Basic Medical Science, Fourth Military Medical University, Xi'an 710032, P. R. China.
Department of Computing, Hong Kong Polytechnic University, Hong Kong 999077, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Apr 25;41(2):272-280. doi: 10.7507/1001-5515.202309068.
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.
现有的一次性身份认证技术无法在整个人机交互会话期间持续保证用户身份的合法性,并且常常需要用户的主动配合,这严重限制了可用性。本研究提出了一种基于超宽带(UWB)生物雷达心脏微运动检测的新型非接触式身份识别技术。通过超宽带生物雷达连续检测人心脏表面积距离维度上的多点微运动回波后,利用二维主成分分析(2D-PCA)提取二维图像矩阵的压缩特征,即每个精确分割的心跳周期中的距离通道-心跳采样点(DC-HBP)矩阵,用于身份识别。在实际测量实验中,基于所提出的多距离单元和2D-PCA特征方案以及两种传统参考特征方案,选择三个典型分类器作为代表,在正常呼吸和屏气两种状态下进行心跳识别。结果表明,本文提出的多距离单元和2D-PCA特征方案具有最佳识别效果。与最优距离单元和整体心跳特征方案相比,我们提出的方案总体平均识别准确率高出6.16%(正常呼吸:6.84%;屏气:5.48%)。与多距离单元和整体心跳特征方案相比,我们提出的方案总体平均准确率提高了27.42%(正常呼吸:28.63%;屏气:26.21%)。本研究有望为认证提供一种无干扰、全天候、非接触和连续识别的新方法。