Pulse rate estimation based on facial videos: an evaluation and optimization of the classical methods using both self-constructed and public datasets

Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis, and it is of great significance for determining the nature of cold and heat in diseases. The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis. However, most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors. A total of 209 participants and 2,435 facial videos, based on our self-constructed Multi-Scene Sign Dataset and the public datasets, were used to perform a multi-level and multi-factor comprehensive comparison. The effects of different datasets, blood volume pulse signal extraction algorithms, region of interests, time windows, color spaces, pulse rate calculation methods, and video recording scenes were analyzed. Furthermore, we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding. We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets. Compared with Fast independent component analysis and Single Channel algorithms, chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness. The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions, and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.