Contact-Less Heart Rate Detection in Low Light Videos

Document Type

Conference Article

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


Heart Rate is considered as an important and widely accepted biological indicator of a person’s overall physiological state. Remotely measuring the heart rate has several benefits in different medical and in computational applications. It helps monitoring the overall health of a person and analyse the effect of various physical, environmental and emotional factors of an individual. Various methods have been proposed in recent years to measure the heart rate remotely using RGB videos. Most of the methods are based on skin color intensity variations which are not visible to the naked eye but can be captured by a digital camera. Signal processing and traditional machine learning techniques have tried to solve this problem using mainly frequency domain analysis of this time varying signal. However these methods are primarily based on face detection and ROI selection in a sufficiently illuminated environment, and fail to produce any output in low lighting conditions which is of utmost importance for the purpose of constant monitoring. Here, we have proposed a 1-dimensional convolutional neural network based framework that processes a magnified version of the time series color variation data in the frequency domain to build an autonomous heart rate monitoring system. With the help of artificial illumination this method can even perform well in low light conditions. Also, we have collected our own dataset that currently contains short frontal face video clips of 50 subjects along with their ground truth heart rate values both in normal and low lighting conditions. We have compared our method with the heuristic signal processing approach to validate its efficacy. (A demo video of the working of our system can be found here )

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