Advances in the Use of rPPG for Non-Invasive Heart Rate Estimation

Despite the growing adoption of remote photoplethysmography (rPPG) for non-contact heart rate monitoring, conventional whole-face approaches face major challenges in real-world scenarios. These include sensitivity to facial movements (e.g., blinking, talking) and uneven illumination, both of which degrade measurement accuracy. To address these limitations, this paper presents a comprehensive evaluation of six unsupervised rPPG algorithms (ICA, GREEN, CHROM, LGI, PBV, and POS) across 21 anatomically defined facial regions, aiming to identify the most robust and precise zones for heart rate estimation. Our results show that specific regions, particularly the forehead, outperform full-face analysis due to their higher vascular density and reduced susceptibility to motion artifacts. Experiments on standard datasets reveal that region-specific methods achieve mean absolute errors below 1.5 beats per minute (BPM), with certain algorithm-region combinations improving accuracy by up to 66% compared to conventional techniques.