FrePL: Frequency-guided Pseudo-labeling for Semi-supervised Remote Physiological Measurement

Remote photoplethysmography (rPPG) has emerged as a promising contactless technology for physiological monitoring, yet its widespread adoption is hindered by the prohibitive cost of acquiring large-scale labeled data. To alleviate this limitation, we propose a semi-supervised remote physiological measurement framework that leverages both labeled and unlabeled facial videos via frequency-guided pseudo-labeling, named FrePL. Specifically, we introduce a pseudo-labeling mechanism to select high-confidence rPPG pseudo-labels via spectral energy concentration. To further mitigate confirmation bias and reduce label noise in the early stages, we adopt a dynamic thresholding strategy with a progressively decaying confidence threshold, which retains more pseudo-labels as the model becomes more robust. A subsequent peak frequency filtering pseudo-label refinement enhances pseudo-label quality by preserving physiological frequency components while suppressing noise. Additionally, we adopt a physiological consistency contrastive learning objective that exploits the intrinsic physiological coherence across augmented views of the same sample, while enforcing discriminative representations between different individuals. Extensive experiments on four benchmark datasets demonstrate that our FrePL not only achieves performance comparable to fully-supervised methods (4.57 mean absolute error (MAE) on VIPL-HR with 20% labeled data), but also significantly outperforms existing semi-supervised approaches.