Human Heart Rate Estimation Using Ordinary Cameras under Natural Movement

Non-contact face-video based human heart rate (HR) estimation has attracted a lot of attentions in recent years. Almost all the state-of-the-art webcam or smartphone based HR estimation methods comprise three main steps: firstly, a region of interest (ROI) on the human face is detected in each video frame, then, the target signal is obtained by fusing multiple raw traces, which are extracted from the RGB channels across all the video frames, finally, HR is estimated by applying frequency analysis approach to the target signal. However, three major drawbacks impede the applicability of the current methods: (1) the performance of ROI detection is susceptible to head motion and facial expression, (2) there is still a lack of well-accepted method for fusing raw traces to form the target signal, and (3) the adopted frequency analysis approaches always provide estimation results with low resolution and high side lobes. To address these issues, we propose a novel HR estimation method which is applicable to ordinary cameras subject to natural head movement or facial expression. The proposed method features ROI detection via facial feature detection and tracking, target signal extraction via Independent Component Analysis (ICA) in the RGB channels, and HR estimation via real-valued iterative adaptive approach (RIAA). Experimental results validate the superiority of our proposed method.