随着信息安全技术的快速发展，人们对身份识别的安全性、便捷性以及高效性的要求不断提高，复杂数字密码、个人身份证件等传统信息安全保护措施已经不能满足人们的需求。目前以指纹、虹膜等为载体的生物识别系统已被广泛应用于金融交易、计算机网络等应用场合，极大地缓解了市场对信息安全保护的迫切需求。但是，这些生物识别系统仍然存在一定的缺陷，比如容易被复制或伪造，因此亟需寻求新的生物特征来弥补这些缺陷或者代替现有生物特征研制新的生物识别系统。本文将医学生理信号中的光电容积脉搏波信号作为研究对象，研究了两种基于光电容积脉搏波信号的身份识别方法：（一）研究一种基于光电容积脉搏波信号P波特征点的身份识别方法。该方法首先采用小波变换法对光电容积脉搏波原始信号进行去噪处理，然后利用极大值极小值法提取光电容积脉搏波信号的P波，并以P波为基准点提取P波间隔、峰谷值等四维时域特征，最后将该四维时域特征作为训练和测试的样本数据输入k最近邻域分类器完成身份识别。实验结果表明，在大训练样本数据的条件下，该方法能够达到85.34%的正确识别率。（二）研究一种基于光电容积脉搏波信号波形的身份识别方法。该方法首先以上述P波为基准点对光电容积脉搏波信号分别进行单周期的分割和插值，然后分别利用带稀疏约束的非负矩阵分解、主成分分析法和核主成分分析法对所有个体的光电容积脉搏波信号的单周期波形进行数据降维以提取特征。最后，将提取的特征作为输入数据，分别利用k最近邻域和支持向量机进行分类识别。相对于上一种身份识别方法，该方法具有如下优点：光电容积脉搏波信号波形特征较光电容积脉搏波信号特征点特征具有抗噪性高、稳定性高以及特异性强等优势；即使在小训练样本条件下，基于光电容积脉搏波信号波形特征的身份识别方法也具有较高的身份识别率。实验结果表明，这种方法的正确识别率高达98.4%。

With the rapid development of information security technology, the traditional information security measures, such as complex digital passwords, personal identification documents, etc. can not meet people’s efficient, safe and convenient requirements. Therefore, currently fingerprint, iris and other biometric systems represented are widely used in financial transactions, computer networks and other applications, which greatly ease the urgent market need for information security protection. However, there are still some drawbacks in these biometric systems, such as easily being copied or forged. Hence, it is urgent to find new biological features to make up for these deficiencies, or take place of existing biological features for new biometric systems. This thesis selects the PPG signal as the research object, and does some research on two main identification methods based on the PPG signal:First, study an identification method based on P-wave fiducial points of the PPG signal. This method completes the original PPG signal preprocessing by the wavelet transform, and extracts the P-waves’ location of PPG signals by maximum-minimum method. Then, four-dimensional time-domain characteristics such as P-wave intervals, the maximum value of the P wave,etc. based on the P-waves’ location, are extracted as the training and test data for KNN classifier. Experimental results show that, under the conditions of large-scale training data, the correct recognition rate of the biometric system can achieve 85.34%.Second, study an identification method based on no-fiducial points of the PPG signal. This method firstly extracts a single period of PPG based on the extracted P-wave as a reference point for signal segmentation, interpolation, and then use the three data dimensionality tools, extended non-negative matrix factorization with sparse constraint, principal component analysis and nuclear component principal component analysis for data dimensionality reduction to extract features. Finally, the extracted features are input in both k-nearest neighbor and support vector machine for human identification. Comparing with the former method, the second method has the following advantages: PPG signal waveform characteristic is of anti-noise, high stability, distinctiveness. Even in small training sample conditions, the identification method based on the PPG signal waveform feature also has a high rate of identification. The experimental results show that this method enjoys the correct recognition rate of 98.4%.