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Measurement of the cardiac pulse from facial video has become an interestingpursuit of research over the last few years. This is mainly due to theincreasing importance of obtaining the heart rate of an individual in anon-invasive manner, which can be highly useful for applications in gaming andthe medical industry. Another instrumental area of research over the past fewyears has been the advent of Deep Learning and using Deep Neural networks toenhance task performance. In this work, we propose to use efficientconvolutional networks to accurately measure the heart rate of user from lowresolution facial videos. Furthermore, to ensure that we are able to obtain theheart rate in real time, we compress the deep learning model by pruning it,thereby reducing its memory footprint. We benchmark the performance of ourapproach on the MAHNOB dataset and compare its performance across multipleapproaches.

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