A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos
This work addresses remote heart rate monitoring for applications in medical or sports environments, but it is incremental as it compares existing methods without introducing new techniques.
The paper tackled the problem of remote heart rate estimation from face videos by comparing four existing methods, including three hand-crafted and one deep learning approach, using the COHFACE database, and found that the learning-based method achieved much better accuracy with a low error rate.
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 subjects 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.