Remote Heart Rate Estimation for Driver Drowsiness Detection

Abstract

Remote heart rate (rHR) estimation, which aims to measure heart activities without any physical contact with the subject, is performed using remote photoplethys- mography (rPPG) and has great potential in many applications. In this paper, we introduce a remote heart rate estimation algorithm to detect driver drowsiness. The proposed method consists of two parts: an rPPGNet for PPG signal prediction from input video frames and an rHRNet for heart rate estimation from predicted PPG signal. Moreover, we apply a skin-based attention module and partition constraint to estimate more accurate rHR. To evaluate the performance of the proposed network, we train and test the proposed network on three public datasets.