Hsu Y.Lin Y.-L.WINSTON HSU2019-07-102019-07-102014978147992892715206149https://scholars.lib.ntu.edu.tw/handle/123456789/413001https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905229621&doi=10.1109%2fICASSP.2014.6854440&partnerID=40&md5=9788359654ad16fc93e97d12faa66612Remote photoplethysmography (rPPG) enables measuring heart rate from recorded skin color variations with consumer cameras. Recent research has aimed to improve the signal strength of color variations caused by heart beat by using independent component analysis (ICA) technique or analyzing chrominance-based model. In this paper, we argue for treating this emerging problem in a novel aspect - proposing a learning-based framework to accommodate multiple and temporal feature and yielding significant and robust improvement. Using support vector regression (SVR) on published chrominance-based feature improves the root mean square error (RMSE) from 22.7 to 7.31 as well as correlation coefficient (CC) from 0.30 to 0.77. With proposed novel multiple feature fusion and multiple segment fusion techniques, we achieved the best estimation result with RMSE 5.48 and CC 0.88. Meanwhile, the proposed framework can be extended to other promising features. ? 2014 IEEE.heart rate; photoplethysmography (PPG); regression learningColor; Heart; Independent component analysis; Mean square error; Photoplethysmography; Correlation coefficient; Heart rates; Independent component analysis(ICA); Multiple feature fusion; Photoplethysmography (PPG); regression learning; Root mean square errors; Support vector regression (SVR); Signal processingconference paper10.1109/ICASSP.2014.68544402-s2.0-84905229621