多特征区域FastICA的非接触式心率检测  

Non-Contact Heart Rate Detection of Multi-Feature Area FastICA

作　　者：王一丁[1] 李飞虎 WANG Yi-Ding;LI Fei-Hu(College of Information Science and Technology,North China University of Technology,Beijing 100144,China)

机构地区：[1]北方工业大学信息学院,北京100144

出　　处：《计算机系统应用》2021年第1期154-161,共8页Computer Systems & Applications

基　　金：北京市科技计划(Z181100009218012)。

摘　　要：人脸视频单特征区域的非接触式心率检测方法,提取的脉搏波信号在视频采集过程中易受运动和光照的影响.为了减弱运动伪差和光照不均对脉搏波信号的干扰,本文提出了一种多特征区域结合快速独立成分分析的非接触式心率信号提取方法.通过人脸特征点算法结合区域中心定位的方法选择多特征区域,保证了视频图像特征区域的稳定性;使用快速独立成分分析实现多特征区域中图像绿色通道血容量变化脉冲信号之间的相互补偿,降低了光照不均匀的影响.在国外公开数据集DEAP上进行实验,实验结果表明,本文方法优于已有基于独立成分分析,独立矢量分析的方法.The pulse wave signal extracted by the non-contact heart rate detection method for a single feature region of a face video is susceptible to motion and light during the video acquisition process.In order to reduce the interference of motion artifacts and uneven illumination on pulse wave signals,a non-contact heart rate signal extraction method with multiple feature regions combined with fast independent component analysis is proposed in this study.Multi-feature regions are selected through the method of facial feature point algorithm combined with positioning regional center to ensure the stability of the feature regions in the video images.Fast independent component analysis is used to achieve the mutual compensation among the green channel blood volume change pulse signal of the images in the multiple feature regions,reducing the effect of uneven lighting.The experiment is performed on the DEAP data set published abroad.The experimental results show that the method in this study is superior to the existing methods based on independent component analysis and independent vector analysis.

关 键 词：血容量变化脉冲信号 快速独立成分分析 人脸特征点检测 多特征区域 

分 类 号：TN911.7[电子电信—通信与信息系统] TP391.41[电子电信—信息与通信工程] R540.4[自动化与计算机技术—计算机应用技术]