【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。更多还原

【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.更多还原