期刊
2020 IEEE 44TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2020)
卷 -, 期 -, 页码 1438-1443出版社
IEEE
DOI: 10.1109/COMPSAC48688.2020.00-53
关键词
Remote Plethysmography; Heart Rate; Face Biometrics; Hand-crafted; Deep Learning
资金
- project: IDEA-FAST (IMI2-2018-15-two-stage) [853981]
- project: PRIMA (ITN-2019) [860315]
- project: TRESPASS-ETN (ITN-2019) [860813]
- project: BIBECA (MINECO/FEDER) [RTI2018-101248-B-I00]
- project: edBB (UAM)
- UAM
This paper presents a comparative evaluation of methods for remote heart rate estimation using face videos, i.e., given a video sequence of the face as input, methods to process it to obtain a robust estimation of the subject's heart rate at each moment. Four alternatives from the literature are tested, three based in hand-crafted approaches and one based on deep learning. The methods are compared using RGB videos from the COHFACE database. Experiments show that the learning-based method achieves much better accuracy than the hand-crafted ones. The low error rate achieved by the learning-based model makes possible its application in real scenarios, e.g. in medical or sports environments.
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