Research on noncontact heart rate detection based on face tracking and BVP signal extraction

Abstract

The noncontact heart rate detection technology is very suitable for daily health monitoring due to its advantages of no contact with the human body, simple operation, and low cost. However, this technique is susceptible to ambient light variation interference and motion artifacts. In addition, most of the existing HR (real-time heart rate monitoring) detection studies based on remote photoplethysmography only focus on illumination variation interference or motion artifacts, and few studies consider both illumination variation interference and motion artifacts. First, a nonparametric skin color segmentation model and support vector machine (SVM) are used in the Cb and Cr components of the YCbCr color space to construct a joint skin color segmentation model specific to the current video sequence, to remove the skin-like background in the video frame in a coarse-to-fine manner. Subsequently, the CamShift algorithm color histogram is constructed within the Cr component and face tracking is performed. It should be noted that the use of SVM to build a skin color segmentation model is based on the following three considerations: first, the skin color segmentation algorithm based on SVM has a relatively fast running speed. Second, considering skin color and background sample information at the same time will help to improve the accuracy of skin color segmentation. Third, SVM can enhance the generalization ability of the learning machine according to the principle of structural risk minimization, which makes the skin color segmentation model based only on video standard frames can also play a better role in the segmentation of subsequent video frames. The experimental results show that the proposed algorithm has higher accuracy for noncontact heart rate detection than the original algorithm; compared with the tracking algorithm in recent years, it has good tracking accuracy and obvious speed advantage.