HBP-net for robust remote heart rate estimation using heartbeat probability

Remote photoplethysmography (rPPG) enables contactless heart rate monitoring but remains vulnerable to motion and lighting changes. We address this by reframing heart rate estimation as a heartbeat detection problem, bypassing the need to reconstruct full blood volume pulse signals. Our approach, HBP-Net, predicts heartbeat probability directly from facial video using a spatiotemporal attention architecture, improving robustness while reducing computational complexity. Evaluated across multiple datasets-including a new motion-challenged benchmark-HBP-Net achieves competitive accuracy under static conditions and maintains performance as motion increases. This shift from signal reconstruction to probabilistic event detection offers a conceptually simpler and more resilient framework for rPPG. The method advances the feasibility of reliable, camera-based vital sign monitoring in real-world settings such as telehealth, fitness tracking, and continuous patient assessment.