Heart Rate Detection From Facial Videos Using A Frequencyconstrained Multilayer Sparse Coding

Abstract

Imaging photoplethysmography (iPPG) can be used to detect heart rates from facial videos. However, it is sensitive to motion disturbances in realistic environments. To address this problem, a frequency-constrained multilayer sparse coding (FCMSC) algorithm is proposed in this paper. Specifically, FCMSC learns a dictionary about iPPG signals from a large set of clean iPPG signals in the training phase, and then reconstructs distorted iPPG signals with the learned dictionary in the testing phase. Compared to previous methods, FCMSC has stronger learning power due to its multilayer structure and better generalization ability because of its frequency constraint. A total of 3630 clips are cut out from the MAHNOB-HCI (a public video dataset) to train and test FCMSC. Experimental results show that FCMSC outperforms state-of-the-art methods in extracting heart rates from facial videos involving motion disturbances.