Identity Authentication in Two-Subject Environments Using Microwave Doppler Radar and Machine Learning Classifiers
Identity Authentication in Two-Subject Environments Using Microwave Doppler Radar and Machine Learning Classifiers
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DOI：
10.1109/tmtt.2022.3197413
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发表时间：
2022-08-22
影响因子：
4.3
通讯作者：
Lubecke, Victor M.
中科院分区：
文献类型：
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作者：
Islam, Shekh M. M.;Boric-Lubecke, Olga;Lubecke, Victor M.
基于多普勒雷达呼吸感知的身份认证正受到关注，因为它既不需要接触也不需要视线，并且不会引发与视频成像相关的隐私问题。先前证明个体识别的研究仅限于孤立的单主体场景。当存在两个等距的主体时，由于反射雷达信号中呼吸运动模式的干扰，识别更具挑战性。在这项研究中，呼吸特征分离技术与机器学习（ML）分类器在功能上相结合，以实现可靠的主体身份认证。提出了一种动态分割算法（峰值搜索和三角测量）的改进版本，它可以提取与气流轮廓相关的可区分特征（呼气面积、吸气面积、吸气/呼气速度和呼吸深度），用于20名不同参与者的中等规模实验，以检验从主体的混合运动中提取个体呼吸特征的可行性。采用具有特征矩阵对角化联合近似的独立成分分析（ICA - JADE）算法从呼吸模式的混合中分离出个体呼吸特征。然后通过集成两种不同的流行机器学习分类器，k - 近邻（KNN）和支持向量机（SVM）对提取的超特征集进行评估，以用于主体认证。在双主体实验中达到了97.5%的准确率，在单主体实验中达到了98.33%的准确率，这超过了先前报道方法的性能。所提出的身份认证方法有多种潜在应用，包括安全/监控、物联网（IoT）应用、虚拟现实和健康监测。
Identity authentication based on Doppler radar respiration sensing is gaining attention as it requires neither contact nor line of sight and does not give rise to privacy concerns associated with video imaging. Prior research demonstrating the recognition of individuals has been limited to isolated single-subject scenarios. When two equidistant subjects are present, identification is more challenging due to the interference of respiration motion patterns in the reflected radar signal. In this research, respiratory signature separation techniques are functionally combined with machine learning (ML) classifiers for reliable subject identity authentication. An improved version of the dynamic segmentation algorithm (peak search and triangulation) was proposed, which can extract distinguishable airflow profile-related features (exhale area, inhale area, inhale/exhale speed, and breathing depth) for medium-scale experiments of 20 different participants to examine the feasibility of extraction of an individual's respiratory features from a combined mixture of motions for subjects. Independent component analysis with the joint approximation of diagonalization of eigenmatrices (ICA-JADE) algorithm was employed to isolate individual respiratory signatures from combined mixtures of breathing patterns. The extracted hyperfeature sets were then evaluated by integrating two different popular ML classifiers, k-nearest neighbor (KNN) and support vector machine (SVM), for subject authentication. Accuracies of 97.5% for two-subject experiments and 98.33% for single-subject experiments were achieved, which supersedes the performance of prior reported methods. The proposed identity authentication approach has several potential applications, including security/surveillance, the Internet-of-Things (IoT) applications, virtual reality, and health monitoring.