One of the known problems in security systems is to identify persons based on certain signatures. Biometrics have been adopted in security systems to identify persons based on some physiological or behavioral characteristics that they own. Photoplethysmography (PPG) is a physiological signal that is used to describe the volumetric change of blood flow in peripherals with heartbeats. The PPG signals gained some interest of researchers in the last few years, because they are used non-invasively, and they are easily captured by the emerging IoT sensors from fingertips. This paper presents a PPG-based approach to identify persons using a neural network classifier. Firstly, PPG signals are captured from a number of persons using IoT sensors. Then, unique features are extracted from captured PPG signals by estimating the Mel-Frequency Cepstral Coefficients (MFCCs). These features are fed into an Artificial Neural Network (ANN) to be trained first and used for identification of persons. A dataset of PPG signals for 35 healthy persons was collected to test the performance of the proposed approach. Experimental results demonstrate 100% and 98.07% accuracy levels using the hold-out method and the 10-fold cross-validation method, respectively.

安全系统中的已知问题之一是基于某些签名来识别人员。在安全系统中已经采用了生物识别技术，以根据其拥有的某些生理或行为特征来识别人员。光电容积描记术（PPG）是一种生理信号，用于描述具有心跳的外周血流的体积变化。PPG信号在过去几年中引起了研究人员的兴趣，因为它们是非侵入性使用的，并且很容易被指尖上的新兴物联网传感器捕获。本文提出了一种基于PPG的方法，使用神经网络分类器识别人员。首先，使用物联网传感器从许多人那里捕获PPG信号。然后，通过估计梅尔频率倒谱系数（MFCC），从捕获的PPG信号中提取独特特征。这些特征被输入到人工神经网络（ANN）中，首先要进行训练，然后用于识别人员。收集了35个健康人的PPG信号数据集，以测试所提出方法的性能。实验结果表明，使用保留方法和10倍交叉验证方法的准确度分别为100％和98.07％。