Enhancing Non-Contact Heart Rate Monitoring: An Intelligent Multi-ROI Approach with Face Masking and CNN-Based Feature Adaptation.

Heart rate (HR) estimation from facial video streams has emerged in the recent years as a promising method of unobtrusive vitals monitoring. Conventional non-contact HR monitoring algorithms like POS, CHROM, ICA are often applied to a single region of interest (ROI), typically the forehead. However, this approach has a lot of disadvantages, such as not utilizing other facial regions, poor tolerance to movement of the subject or face. To address this, we propose a MultiROI approach with face Masking and CNN-based facial feature adaptation. We introduce an novel face masking technique method using facial landmarks alone, effectively eliminating non-skin pixels like background, hair, eyes, lips, and eyebrows. Additionally, a CNN model was designed to classify individuals based on facial features, dynamically adjusting ROI positions and ROI numbers accordingly. The proposed comprehensive approach significantly reduced the Mean Absolute Error (MAE) in HR measurement by 58.2%, 47.1%, and 33.2% for POS, CHROM, and ICA algorithms respectively, when compared to the traditional single ROI approach. The multi-ROI approach can thus improve measurement reliability and robustness to motion.