Multimodal Heartbeat Rate Estimation from the Fusion of Facial RGB and Thermal Videos

Measuring Heartbeat Rate (HR) is an important tool for monitoring the health of a person. When the heart beats the influx of blood to the head causes slight involuntary movement and subtle skin color changes, which cannot be seen by the naked eye but can be tracked from facial videos using computer vision techniques and can be analyzed to estimate the HR. However, the current state of the art solutions encounter an increasing amount of complications when the subject has voluntary motion on the face or when the lighting conditions change in the video. Thus the accuracy of the HR estimation using computer vision is still inferior to that of a physical Electrocardiography (ECG) based system. The aim of this work is to improve the current non-invasive HR measurement by fusing the motion-based and color-based HR estimation methods and using them on multiple input modalities, e.g., RGB and thermal imaging. Our experiments indicate that late-fusion of the results of these methods (motion and color-based) applied to these different modalities, produces more accurate results compared to the existing solutions.