基于面部视频分析的生命体征检测

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。 

Vital signs detection via facial video analysis

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.