The FVBPSR-Mamba system achieved superior accuracy and robust generalization for remote heart rate measurement from facial videos compared to traditional and state-of-the-art deep learning models.
Does the FVBPSR-Mamba system improve the accuracy of remote heart rate measurement from facial videos compared to traditional and SOTA deep learning models?
The FVBPSR-Mamba system effectively recovers rPPG signals from facial videos for remote heart rate measurement, demonstrating improvements over existing methods.
Remote photoplethysmography (rPPG) is an emerging non-contact modality that estimates vital signs by extracting blood volume pulse (BVP) signals from facial video sequences. Currently, the primary challenges in this field lie in extracting high-quality rPPG signals from video segments characterized by significant spatiotemporal redundancy and in accurately capturing the periodic patterns of rPPG within a long-term context.In this study, an end-to-end high-quality rPPG signal recovery system, termed FVBPSR-Mamba, is proposed for remote heart rate measurement via video. In this framework, a Multi-scale Hierarchical Spatial Mamba (MHSM) module is designed to explore subtle rPPG signals from multi-scale spatiotemporal receptive fields while enhancing spatial perception and temporal context understanding. Furthermore, a frequency-domain noise reduction module is incorporated to strengthen the quasi-periodic patterns of rPPG and mitigate interference from irrelevant noise. Extensive benchmarking against both traditional methods and state-of-the-art (SOTA) deep learning models demonstrates that the proposed approach achieves superior accuracy and robust generalization in both intra-dataset and cross-dataset evaluations on the UBFC-rPPG and UBFC-Phys datasets. In conclusion, the FVBPSR-Mamba system can effectively recover rPPG signals from facial videos, facilitating the remote measurement of vital signs such as heart rate. • Propose a FVBPSR-Mamba system for reconstructing rPPG signals from facial videos. • Introduce a Multi-scale hierarchical Spatial Mamba to adaptively identify ROI. • Develop a frequency-domain noise filtering to enhance the signals periodic patterns. • The proposed FVBPSR-Mamba system demonstrates improvements in heart rate estimation.
Zhang et al. (Thu,) conducted a other in Remote heart rate measurement. FVBPSR-Mamba system vs. Traditional methods and state-of-the-art deep learning models was evaluated on Accuracy and robust generalization in heart rate estimation. The FVBPSR-Mamba system achieved superior accuracy and robust generalization for remote heart rate measurement from facial videos compared to traditional and state-of-the-art deep learning models.