A Modified POS-based rPPG with Real-time Deep Facial ROI Tracker and Pose Constrained Kalman Filter: M-POS rPPG with DFT and PCKF.

Contactless measurement of heart rate(HR) based on videos can be essential for tele-monitoring medical system and public security. This paper proposed a modified POS-based remote photoplethysmography(rPPG) algorithm by replacing alpha tuning with singular value decomposition(SVD) to eliminate the specular reflection's spatiotemporal characteristic. What's more, we propose a novel landmark-based approach for a deep facial ROI tracker and face pose constrained Kalman filter to continuously and robustly track target facial ROIs for estimating HR from large head motion disturbances in rPPG. We demonstrate through experimental comparisons that the proposed method is more robust and accurate than the state-of-the-art rPPG-based methods in stable state. The mean absolute error (MAE) of HR estimation is 2.15 BPM lower than the POS and the AUC of HR estimation accuracy is 0.04 higher than POS in stable state. In motion state, the performance of our modified is a little bit better than POS.