基于面部视频分析的生命体征检测  

Vital signs detection via facial video analysis

作　　者：陈辉[1] 郑秀娟 倪宗军 张昀[2] 杨晓梅[1] CHEN Hui;ZHENG Xiujuan;NI Zongjun;ZHANG Yun;YANG Xiaomei(College of Electrical Engineering,Sichuan University,Chengdu 610065,China;School of Electronic and Information Engineering,Xi'an Jiaotong University,Xi'an 710049,China)

机构地区：[1]四川大学电气工程学院,成都610065 [2]西安交通大学电子与信息学院,西安710049

出　　处：《北京航空航天大学学报》2020年第9期1770-1777,共8页Journal of Beijing University of Aeronautics and Astronautics

基　　金：成都市重点研发支撑计划技术创新研发项目(2020-YF05-00056-SN)。

摘　　要：在面部视频中提取生命体征相关的生理信号时易受环境光和受试者头部运动的影响,为了降低外界干扰并提高生命体征检测的准确度,提出了一种联合集合经验模态分解(EEMD)算法与信号质量检测的面部视频分析方法,用于精确检测人体的心率与呼吸频率等生命体征。通过公开数据集进行实验验证,实验结果表明,所提方法相较于目前已有的常用信号处理方法能够得到更精确的心率与呼吸频率的估计值,所得估计值与标准值的相关系数分别高于0.9和0.8。同时,所提方法将为实时活体人脸识别提供一种思路,也有助于丰富监控视频智能分析的应用研究。To detect the physiological signals related to vital signs via facial video is easily affected by ambient lights and head motions.In order to reduce the disturbance and increase the accuracy of estimations of vital signs,this paper proposes a facial video analysis method that combines Ensemble Empirical Mode Decomposition(EEMD)algorithm and signals quality detection to accurately detect vital signs such as the heart rate and respiratory rate of human beings.The performance of the proposed method is validated by comparing it with the existing signal processing techniques in a public dataset.The experimental results show that the proposed method can obtain more accurate estimates of heart rate and respiratory rate than the existing methods.The correlation coefficients between the estimates and the golden standards are higher than 0.9 and 0.8,respectively.The vital signs detection method has the potential to benefit real-time living face recognition and intelligent surveillance video analysis.

关 键 词：面部视频 成像式光电容积描记法 集合经验模态分解(EEMD) 信号质量检测 抗干扰 

分 类 号：V221.3[航空宇航科学与技术—飞行器设计] TB553[理学—物理]