Region of Interest Analysis Using Delaunay Triangulation for Facial Video-Based Heart Rate Estimation

Selecting a reliable region of interest (ROI) is essential when estimating the blood volume pulse signal (BVP) through contactless remote photoplethysmography (rPPG) using facial videos. The face detection, facial landmark, and skin segmentation algorithms are commonly used for ROI selection. However, the current method of ROI selection primarily relies on human experience, and there are limited studies that investigate the impact of different facial regions on the remote heart rate estimation. In this paper, we employ the Delaunay triangulation to analyze facial ROIs. We use the Mediapipe face landmark model to annotate 468 facial feature points. Subsequently, the Bowyer-Watson algorithm is employed to partition the face into 898 triangular ROIs based on the set of feature points. We evaluate the performance of each triangular ROI by the error in the estimated heart rate and select multiple regions that demonstrate superior performance from the triangular regions to form the recommended ROI. Additionally, we introduce a data-driven approach (DD-ROI) that dynamically segments skin region as ROI using the set of triangular ROIs. Various motion-robust rPPG methods, including chrominance (CHROM), plane orthogonal to skin (POS), filtered green signal (GREEN), independent component analysis (ICA), local group invariance (LGI), orthogonal matrix image transformation (OMIT), and normalized blood volume pulse vector (PBV), are used to validate the proposed method. Comparing with commonly used ROIs such as face detection rectangular, the center 60% of the face detection rectangular, skin segmentation, and the cheek regions, DD-ROI improves the performance of existing rPPG technologies on both intra- and inter-dataset tasks, effectively reducing the error in remote heart rate estimation.