Non-Contact Heart Rate Monitoring from Face Video Utilizing Color Intensity

Abstract

Heart Rate is a crucial physiological parameter that provides basic information about the state of the human body in the cardiovascular system, as well as in medical diagnostics and fitness assessments. At present day, it has been demonstrated that facial video-based photoplethysmographic signal captured using a low-cost RGB camera is possible to retrieve remote heart rate. Traditional heart rate measurement is mostly obtained by direct contact with the human body, therefore, it can result inconvenient for long-term measurement due to the discomfort that it causes to the subject. In this paper, we propose a non-contact-based remote heart rate measuring approach of the subject which depends on the color intensity variation of the subject's facial skin. The proposed method is applied in two regions of the subject's face, forehead and cheeks. For this, three different algorithms are used to measure the heart rate. i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA). The average accuracy for the three algorithms utilizing the proposed method was 89.25% in both regions. It is also noteworthy that the FastICA algorithm showed a higher average accuracy of more than 92% in both regions. The proposed method obtained 1.94% higher average accuracy than the traditional method based on average color value.

Robust Pulse Rate Measurements from Facial Videos in Diverse Environments

“…Remote non-contact pulse rate measurements are appealing for both commercial and academic applications [ 21 , 26 ]. Several papers from the past few years have proposed a color-based method for non-contact pulse rate measurements using general cameras to solve the problem of contact-type measuring devices [ 12 , 14 , 38 ].…”

“…There are numerous ways to calculate pulse wave and rate. The methods of calculating pulse rate can be summarized as follows: (1) by applying ICA to RGB color data calculated in ROI and FFT to pulse-related frequency bands [ 1 ], (2) by detecting a peak point on a signal calculated by applying ICA [ 33 ], (3) by detecting a peak point on a pulse wave calculated by applying BPF by setting a cut-off frequency in a pulse-related frequency band [ 10 ], (4) by applying PCA to RGB color data calculated in ROI and FFT to pulse-related frequency bands [ 26 ], etc. When measuring pulse rates using color data of ROI from facial images, the majority of previous studies have allowed subjects to remain stationary and have only considered a constant indoor environment with minimal illumination changes.…”

“…To consider the problem of illumination changes in pulse rate measurements using facial videos, previous studies used methods of: (1) taking a facial video in a limited situation where light sources (LED, natural light, etc.) and subjects were immobilized [ 1 , 5 , 18 , 20 , 26 ], (2) using H, S, and V color data calculated by converting RGB color models of detected ROI to HSV color models, (3) using each component of the separated RGB by applying ICA to the RGB color model [ 12 , 26 , 33 ]. In order to solve the problem associated with the subject’s fine tremor, a specific number of landmarks were set in the detected face area, and a pulse rate measurement experiment was conducted using RGB color data from the ROI detected in the set face [ 10 , 18 , 19 , 21 ].…”

“…Previous research has demonstrated various methods for measuring pulse rate by applying independent component analysis (ICA), principal component analysis (PCA), fast Fourier transform (FFT), band pass filter (BPF) to RGB color data calculated in the region of interest (ROI) of facial images [ 1 , 7 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ], and by analyzing head movement and blinking [ 12 , 14 , 35 ] under controlled laboratory conditions. However, in external environments (outdoor bench, car, drone), the pulse rate cannot be accurately measured due to factors such as the user’s fine body tremor, illumination changes, non-detection of the face, etc.…”

Assessment of ROI Selection for Facial Video-Based rPPG

“…rPPG is a contrast between specular reflection and diffuse reflection that occurs when light hits the skin. Specular reflections are pure light reflections from the skin, while diffuse reflections are reflections due to absorption and scattering of skin tissue that depend on changes in blood volume [ 22 ].…”