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• 图像处理与计算机视觉 • 上一篇    下一篇

基于低秩稀疏矩阵分解的非接触心率估计

  

  1. 1. 上海师范大学信息与机电学院,上海 200234;
    2. 天津大学智能与计算学部计算机科学与技术学院,天津 300050
  • 出版日期:2020-02-29 发布日期:2020-03-11
  • 基金资助:
    上海市人工智能创新发展专项(2018-RGZN-01013)

Non-touch heart rate estimation based on the low-rank and sparse matrix decomposition

  1. 1. School of Information and Mechatronics, Shanghai Normal University, Shanghai 200234, China;
    2. School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin 300050, China
  • Online:2020-02-29 Published:2020-03-11

摘要: 心率检测作为一项重要的生理检测指标,在医学健康、刑侦检测、信息安全等方
面具有重要应用。计算机视觉领域近期的研究表明,心率信号可以通过摄像头捕捉的视频予以
获取。现有的研究方法在理想的实验环境下已取得较好的效果,然而在自然状态面部旋转以及
出现各种噪声(阴影、遮挡)时鲁棒性较弱。通过检测人脸的关键点,获得面部区域的感兴趣,
避免因面部旋转引入检测误差,在现有模型的基础上提出一种基于低秩稀疏矩阵分解的非接触
式心率估计模型,对频域血液体积脉冲(BVP)信号矩阵实现去噪处理,解决使用摄像头非接触
式获取心率信号时存在的问题。实验显示,该模型在MAHNOB-HCI 数据集上实现了3.25%的
误差比均值,优于现有的模型。

关键词: 低秩稀疏矩阵分解, 非接触式, 心率信号估计, 人脸关键点检测, 噪声, 鲁棒性

Abstract: Heart rate detection, as a vital physiological parameter, plays an important role in medical
care, criminal investigation andinformation security, etc. Current studies on computer vision areas
have shown that heart rate signals can be obtained from videos captured by a normal webcam. The
current method can achieve relatively more desirable results in ideal experimental environments,
while the robustness of it is poorer in natural conditions when there is head shaking, noise and
shadow. In this study, we captured the region of interest by detecting the face landmarks, to reduce the
interference of the detection errors caused by the head shaking. And based on low-rank and sparse
matrix decomposition, this paper proposes a non-touch heart rate estimation model to denoise the
blood volume pulse (BVP) signal matrix in the frequency domain, so as to tackle the problem arising
from capturing heart rate signals by cameras in a non-touch way. We tested our model on the dataset
of MAHNOB-HCI and the results showed that the proposed model outperforms with 3.25% error
ratio means.

Key words: low-rank and sparse matrix decomposition, non-touch, heart rate estimation, face
land-mark detection,
noise, robustness