Abstract—Remote Photoplethysmography (rPPG) enables non-contact heart rate monitoring from facial videos but it is highly susceptible to motion artefacts, illumination changes, and sensor noise. A framework combining the Plane Orthogonal-to-Skin (POS) method and Singular Spectrum Analysis (SSA) was proposed in this work to address these challenges by first projecting normalized Red Green Blue (RGB) signals onto a skin-tone–orthogonal subspace to suppress illumination and motion distortions and then decomposing the resulting signal into components that isolate physiologically meaningful oscillations. Evaluation on the PFF and UBFC-Phys dataset demonstrates that this approach consistently outperforms conventional single-channel, statistical, and chrominance-based methods by achieving a mean absolute error of 4.99 beats per minute (bpm) and correlation of 0.76 on PFF, and a mean absolute error of 4.11 bpm with correlation of 0.86 on UBFC-Phys. Furthermore, the comparison with results reported in the existing literature indicates that the proposed framework achieves competitive accuracy relative to popular learning-based rPPG approaches. These findings indicate that integrating chrominance projection with adaptive temporal decomposition significantly improves robustness and accuracy for contact-free heart rate estimation.
 
Keywords—facial video-based remote Photoplethysmography (rPPG), Plane Orthogonal-to-Skin (POS), Singular Spectrum Analysis (SSA)


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