A Multiresolution Method for Non-Contact Heart Rate Estimation Using Facial Video Frames

Abstract

In recent years, camera-based non-contact heart rate (HR) measurement technology has grown immensely. The system captures the reflection of light from the facial tissues and lead to the formation of a remote photoplethysmogram (rPPG) signal that can be used to measure physiological parameters for cardiac health assessment. Due to environmental interferences, extraction of a reliable rPPG signal is a challenging task and thus, requires a robust denoising algorithm. In this paper, a discrete wavelet transform (DWT)-based multiresolution method is used to remove the noises from the video frames caused due to illumination variation and motion artifacts. Subsequently, rPPG signal is extracted and HR is measured from two region of interests (ROIs), facial and forehead regions. The study evaluates the performance of the proposed method on each of the RGB color channels from both the ROIs. The performance results for the COHFACE dataset show that the proposed method works well for the estimation of HR values. Furthermore, they reveal that the forehead region on the green channel is more suitable for HR measurement.