Skin Detection and Tracking for Camera-Based Photoplethysmography Using a Bayesian Classifier and Level Set Segmentation

Abstract

Camera-Based Photoplethysmography is a measuring technique that permits the remote assessment of vital signs by using cameras. The face is the preferred area of measurement (region of interest: ROI) that has to be selected automatically for convenient application. Most works use common face detection algorithm for this purpose. However, these approaches often fail if the face is partly occluded or distorted. In this work, we propose an automatic method for ROI detection and tracking that does not rely on facial features. First, a Bayesian skin classifier was applied. Second, the detected areas were refined and tracked by level set segmentation. We tested our method on videos of 70 patients. The determined ROIs were used for signal extraction and heart rate (HR)estimation. The results showed that our method can detect and track suitable skin regions. We achieved a median HR detection rate of 80% which was only 6% lower than when applying manually defined ROIs.