EVM-CNN: Real-Time Contactless Heart Rate Estimation from Facial Video
This work addresses the need for noninvasive health monitoring by improving accuracy and processing time for heart rate estimation from facial videos, though it appears incremental as it builds on existing spatial decomposition and filtering methods.
The paper tackles real-time, contactless heart rate estimation from facial video by introducing a framework that combines spatial and temporal filtering with a convolutional neural network, achieving better performance on the MMSE-HR dataset in average and short-time HR estimation with high consistency to ground truth.
With the increase in health consciousness, noninvasive body monitoring has aroused interest among researchers. As one of the most important pieces of physiological information, researchers have remotely estimated the heart rate (HR) from facial videos in recent years. Although progress has been made over the past few years, there are still some limitations, like the processing time increasing with accuracy and the lack of comprehensive and challenging datasets for use and comparison. Recently, it was shown that HR information can be extracted from facial videos by spatial decomposition and temporal filtering. Inspired by this, a new framework is introduced in this paper to remotely estimate the HR under realistic conditions by combining spatial and temporal filtering and a convolutional neural network. Our proposed approach shows better performance compared with the benchmark on the MMSE-HR dataset in terms of both the average HR estimation and short-time HR estimation. High consistency in short-time HR estimation is observed between our method and the ground truth.