Long Short-Term Memory Deep-Filter in Remote Photoplethysmography

Remote photoplethysmography (rPPG) is a recent technique for estimating heart rate by analyzing subtle skin color variations using regular cameras. As multiple noise sources can pollute the estimated signal, post-processing techniques, such as bandpass filtering, are generally used. However, it is often possible to see alterations in the filtered signal that have not been suppressed, although an experienced eye can easily identify them. From this observation, we propose in this work to use an LSTM network to filter the rPPG signal. The network is able to learn the characteristic shape of the rPPG signal and especially its temporal structure, which is not possible with the usual signal processing-based filtering methods. The results of this study, obtained on a public database, have demonstrated that the proposed deep-learning-based filtering method outperforms the regular post-processing ones in terms of signal quality and accuracy of heart rate estimation.