PhysFFTFormer: A Frequency Domain-based Vision Transformer for Efficient Remote Physiological Measurement

Remote Photoplethysmography (rPPG) is a non-contact technique for extracting physiological signals from facial videos. Recently, Transformer-based architectures have exhibited remarkable performance in rPPG estimation, owing to their excellent long-range spatial-temporal modeling capacities. However, challenges persist in applying Transformer-based rPPG methods, where quadratic computational costs and inadequate feature modeling diversity remain formidable. To address these challenges, we leverage the power of the Frequency Domain-based Vision Transformer and propose an end-to-end model PhysFFTFormer. Specifically, by integrating customized Frequency-Domain Spatiotemporal Self-Attention and Frequency-Domain Discriminative Feed-Forward modules, PhysFFTFormer efficiently captures spatial-temporal dependencies with reduced computational complexity. To extract rich and diverse feature representations, we design a dual-pathway architecture to utilize both raw and differential video frames. Furthermore, the Frequency-Domain Spatiotemporal Cross-Attention module is introduced to enhance information exchange and enable feature complementation between the two pathways. Extensive experiments on multiple benchmark datasets demonstrate PhysFFTFormer’s state-of-the-art performance, robustness, and potential for real-world non-contact health monitoring.