PhysQual: A quality-aware learning framework for robust remote photoplethysmography

Remote photoplethysmography (rPPG) has emerged as a promising technology for non-contact heart rate estimation from facial videos. However, most existing methods produce deterministic estimates without reporting reliability which is a critical limitation for real-world deployment. We present PhysQual, a framework that treats trustworthiness as a first-class output by jointly estimating blood volume pulse (BVP) signals and multi-dimensional quality scores including motion, illumination, skin visibility, and temporal consistency available at inference time. We introduce three complementary strategies: (1) Physics-Guided Quality by estimating reliability from environmental dynamics; (2) Performance-Guided Quality by learning a proxy to identify prediction errors due to latent failures, and (3) Uncertainty-Guided Quality by capturing input-dependent uncertainty through learned variance. Experiments on UBFC-rPPG and TokyoTech datasets demonstrate that PhysQual significantly improves accuracy and reliability awareness. Our framework achieves a 52% error reduction in similar-domain tests (2.68 → 1.28 bpm) and 16% in cross-domain scenarios (3.26 → 2.73 bpm), outperforming established baseline models. Further, error-retention analysis shows that PhysQual enables tunable reliability at inference time, reducing cross-domain error by 34.6% through targeted rejection of unreliable samples. These results highlight PhysQual’s flexibility for robust rPPG deployment across diverse operating scenarios.