  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.
