Wavelet-Based Spectral Feature Gating for Near-Infrared Remote Photoplethysmography Estimation

Near-infrared (NIR) video-based remote photoplethysmography (rPPG) enables physiological measurement under low-light conditions using monochrome facial videos. NIR videos lack chrominance-based cues, while weak cardiac variations remain entangled with motion, illumination, and imaging noise. To address these limitations, we propose a wavelet-based spectral feature gating (WSFG) module for NIR-based rPPG estimation. The WSFG module applies a two-level stationary wavelet transform (SWT) to decompose intermediate temporal features without temporal downsampling. Specifically, learnable spectral gates regulate each sub-band contribution and reconstruct branch-specific temporal features through inverse SWT. Three specialized branches preserve cardiac variations, suppress nuisance components, and model relative optical attenuation from low-frequency reference features. Experiments on egoPPG-DB dataset demonstrate that the proposed method achieves the lowest mean absolute error (MAE) and mean absolute percentage error (MAPE) of 8.56 bpm and 9.86%, respectively. Further analysis highlight the effectiveness of frequency-aware feature modeling for NIR-only rPPG estimation.