Remote Photoplethysmography from Low Resolution videos: An end-to-end solution using Efficient ConvNets

TL;DR

This paper presents an end-to-end deep learning approach using efficient convolutional networks to accurately and in real-time measure heart rate from low-resolution facial videos, with model compression for practical deployment.

Contribution

It introduces a novel, efficient deep learning model for remote photoplethysmography from low-res videos and demonstrates real-time capability through model pruning.

Findings

Achieves accurate heart rate measurement on MAHNOB dataset

Outperforms existing methods in low-resolution scenarios

Enables real-time processing with compressed models

Abstract

Measurement of the cardiac pulse from facial video has become an interesting pursuit of research over the last few years. This is mainly due to the increasing importance of obtaining the heart rate of an individual in a non-invasive manner, which can be highly useful for applications in gaming and the medical industry. Another instrumental area of research over the past few years has been the advent of Deep Learning and using Deep Neural networks to enhance task performance. In this work, we propose to use efficient convolutional networks to accurately measure the heart rate of user from low resolution facial videos. Furthermore, to ensure that we are able to obtain the heart rate in real time, we compress the deep learning model by pruning it, thereby reducing its memory footprint. We benchmark the performance of our approach on the MAHNOB dataset and compare its performance across multiple approaches.