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Author(s): Kokila Bharti Jaiswal


Address: Department of Electronics and Communication, National Institute of Technology, Raipur.

*Corresponding Author:

Published In:   Volume - 36,      Issue - 1,     Year - 2023

Cite this article:
Kokila Bharti Jaiswal (2023). Development of Non-Invasive Technique for Heart Rate Detection Using Facial Videos. Journal of Ravishankar University (Part-B: Science), 36(1), pp. 12- 17.

Development of Non-Invasive Technique for Heart Rate Detection Using Facial Videos

Kokila Bharti Jaiswal1

1Department of Electronics and Communication, National Institute of Technology, Raipur

 *Corresponding Author:


Mortality rate in Chhattisgarh state due to ischemic heart disease is 43.6% and growing exponentially every year. Early detection of cardiac health plays a major role in decreasing this rate. Due to the insufficient hospitals and accessibility of the dedicated equipment, remote health monitoring has become quite inevitable after SARC-CoV-2 pandemic. Due to its excellent capability is it going to be cardiac rate measurement method of future. However, the difficulty in HR measurement is that, it gets affected with noise very easily because the amplitude of physiological signal is very weak. remote Photoplethysmography (rPPG) is a technique to measure the cardiac activity in a contact-less manner using digital cameras. However, the HR estimation suffers from two major artifacts, motion artifact and illumination artifact. Denoising of rPPG signal is a fundamental problem and needs to be addressed very carefully. In this article we have proposed a novel HR estimation network using a combination of wavelet decomposition and Convolutional Neural Network (CNN). This approach provides distinct features at different frequency levels, which facilitates the removal of noisy signal. Performance evaluation of the proposed method is done on self-collected dataset. Lower values of RMSE and MAE proves the efficacy of the proposed method.

Keywords: remote Photoplethysmography(rPPG), Heart rate, Telehealth monitoring, CNN

1. Introduction

Cardiovascular diseases (CVDs) are leading cause of mortality worldwide, accounting for 17.3 million deaths per year, a number that is expected to grow to more than 23.6 million by 2030 [1]. More specifically if we talk about Chhattisgarh state the mortality rate due to ischemic heart disease, diabetes and Chronic obstructive pulmonary disease (COPD) are the major cause of morbidity.  It contributes to 53.82% of total Disability-Adjusted Life Years (DALYs) [2]. To provide medical attention to this serious disease, mere number of i.e, 26 district hospitals are available in the state. Total Population of the state is 2.55 crore amongst which 76.76% belongs to rural areas. Availability of public healthcare providers in district healthcare system is 5 per 10,000. The accessibility of private hospitals is too expensive to be borne by common man.  All these statistics clearly says that there is an immediate requirement of some alternate measure. Early detection of heart rate can be a boon to prevent cardiovascular disease. Dependency on ECGs for cardiac health check-ups can be eliminated by the use of various types of contact-based devices such as wrist watches and waist bands.  These devices work on the principle of photoplethysmography (PPG). But these contact-based method suffers from the limitation that it cannot be used with a person suffering from skin disease and in the case of neonatal. Contact based methods cause skin allergies and may cause discomfort to the patient or person. Over the last few years cardiovascular status from using camera has gained immense popularity. These technique works on the principle of remote photoplethysmography (rPPG). It works on the principle that when the person face is illuminated with light source, some amount of light gets reflected from the surface of skin which is called specular reflection whereas some amount of light penetrates deep inside the skin and reflection occurs due to the presence of hemoglobin and melanin which is called diffuse reflection as shown in Fig. 1. The reflection due to volume of blood is in synchronization with the heartbeat. Therefore, by capturing the reflected amount of light through camera and applying various signal processing methods, we can measure the heart rate of a person based only on the facial video.

Such measurement comes under a category of telemedicine, where a person located in remote areas without any medical representatives, without any hospitals facilized with expensive machines will be able to track the cardiac functionalities. Such devices are very helpful for early detection of functionality of heart and if any anomaly is seen the person can rush to the hospital immediately for proper medical attention.  The technique was first discovered by Verkyuesse et al. in 2008 [3] since then many advancements have been made. Many Blind source separation (BSS)methods [4,5] have been developed considering the fact that the noise and the rPPG signal are linearly separable. Later on, method such as CHROM [7] and POS [6] are developed which are color intensity-based methods. EVM-CNN [8] exploits the power of CNN to make robust system for the detection of heart rate. Here in this article, we have tried to overcome the limitations of the previously proposed methods for motion scenarios.

Fig. 1 Working principle of rPPG [6]


2. Experimental Details:

Video is acquired using simple mobile phone camera (Samsung Galaxy-SM-F127G/DS) with a frame rate of 30fps and resolution of 1080 x 1920 in an RGB format. Video is recorded in two different conditions. First in closed Computer Vision Lab at NIT, Raipur, illuminated with fluorescent tube producing visible light. Second recording is done outdoor in the campus itself, where the subjected is illuminated with natural sunlight varying by the shades of cloud. In both the recordings the person is subjected to three conditions:

Resting position with deep breath

Showing change in facial emotions (smile, laugh and anger)

Elevated heart rate condition due to strenuous physical activity

All the data are recorded as 20 sec videos. Distance between the subject and camera is set to be 1metre.  For ground truth collection we have used USM001 fingertip pulse oximeter attached to the index finger of the subject. Sample frames of the self-collected dataset is shown in Fig. 2.

Fig. 2 Sample frames of the self-collected dataset


3. Method

The basic flow diagram of the rPPG signal extraction through video can be depicted from Fig. 3. Here the frames are extracted from video of a subject. We are interested only on the face region. The frames are cropped to get the face region which forms the ROI. The ROI is tracked using Kanade-Lucas -Tomasi (KLT) algorithm.  Here only the green channel is considered for HR estimation owing to the fact that the absorption of light in green color range i.e, wavelength 550nm-590nm is maximum. Once the ROI is detected a wavelet transform is applied to each ROI to get the subbands. This multiresolution decomposition of G channel enables the identification of noise frequency, which can be removed using SureShrink threshold. The thresholded bands can now be concatenated to form spatiotemporal map.  The benefit of constructing feature map is that it is now focusing only the information related to HR and discarding any noises which occurs due to motion by natural movements of face. This spatiotemporal map is now applied as an input to Convolutional Neural Network (CNN) for HR estimation. Owing to the success of CNN in various tasks such as age prediction, disease progression etc , compelled us to use CNN network like ResNet-18 for HR estimation problem.  The problem of HR estimation is considered as the regression problem. The ResNet18 is trained using Adam optimizer and at the output we have used linear activation function to get the single HR value. Learning rate is set as 0.005. L1 loss function is used to minimize the error. The network is trained on publicly available UBFC-rPPG [9] dataset and is tested on self-collected dataset.

Fig. 3 Illustration of the flow diagram of the proposed method.


4. Observation and Results

The performance of the proposed network is determined based on the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) given in Eqn. 1 and 2. It can be clearly seen from the Fig. 4, that the predicted value closely follows the ground truth value of HR. Also, from Table -1 it can be concluded that the lower values of RMSE and MAE indicates the better accuracy of the proposed network for the detection of HR.






Where, HRe is the estimated heart rate and HRgt is the ground truth measured from pulse oximeter.

Table-1 Performance evaluation of subjects from self-collected dataset





















Fig. 4 Ground truth and Predicted HR value of subject -1


5. Discussion

rPPG provides us the convenience to measure the heart rate of a person using simple RGB facial video of a person. The inception of rPPG may be a decades ago but its necessity surges in the Covid era. Due to cost effectiveness and accuracy, it is continuing to be one of the best technologies to be used for monitoring cardiac health.  But the main challenge is removal of unwanted signal or noisy signal due to movement of head or natural variations in facial expression. In this article a network is proposed for the estimation of heart rate under motion scenarios. In this work we have effectively denoised the rPPG signal using wavelet transform. Also, the used of Convolution Neural Network makes the network to work on large dataset and improves the accuracy of HR estimation. In future the telehealth monitoring may become a part of daily routine of each and every individual.  Such technology plays an important role in reducing the mortality rate due to cardiac diseases to a large extent.



This research has been supported by National Institute of Technology, Raipur. The research is performed at Computer Vision Lab of Electronics and Telecommunication Department. Special thanks to my research advisor Dr. T. Meenpal for his constant support and Computer Vision Lab research scholars Nitish Kumar, Madhu Oruganti and Deeksha Sahu for giving their consent to collect data samples.



[1] Bansal, A., & Joshi, R. (2018). Portable out‐of‐hospital electrocardiography: A review of current technologies. Journal of arrhythmia34(2), 129-138.

[2] National Health Systems Resource Centre,

[3] Verkruysse, W., Svaasand, L. O., & Nelson, J. S. (2008). Remote plethysmographic imaging using ambient light. Optics express16(26), 21434-21445.

[4] Poh, M. Z., McDuff, D. J., & Picard, R. W. (2010). Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics express18(10), 10762-10774.

[5] Kranjec, J., Beguš, S., Geršak, G., & Drnovšek, J. (2014). Non-contact heart rate and heart rate variability measurements: A review. Biomedical signal processing and control13, 102-112.

[6] Wang, W., Den Brinker, A. C., Stuijk, S., & De Haan, G. (2016). Algorithmic principles of remote PPG. IEEE Transactions on Biomedical Engineering64(7), 1479-1491.

[7] De Haan, G., & Jeanne, V. (2013). Robust pulse rate from chrominance-based rPPG. IEEE Transactions on Biomedical Engineering60(10), 2878-2886.

[8] Qiu, Y., Liu, Y., Arteaga-Falconi, J., Dong, H., & El Saddik, A. (2018). EVM-CNN: Real-time contactless heart rate estimation from facial video. IEEE transactions on multimedia21(7), 1778-1787.

[9] S. Bobbia, R. Macwan, Y. Benezeth, A. Mansouri, J. Dubois, "Unsupervised skin tissue segmentation for remote photoplethysmography", Pattern Recognition Letters, 2017.


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