Heart rate (HR) is a critical indicator of an individual’s health, serving as a key metric for detecting potential cardiac issues. This paper explores a method for real-time heart rate measurement using RGB camera footage, aimed at general health monitoring. The proposed method utilizes a convolutional neural network (CNN) to generate a 3D mesh of the subjects’ facial features. The movement over time of the points in this mesh is used to compute a signal that captures the small pulsatile movements corresponding to the mechanical motion of blood being pumped through the veins. This signal is filtered, and motion sources are separated using principal component analysis (PCA). The most periodic component, the one with the highest frequency, is considered to correspond to the heart rate, and it’s frequency is used to estimate the heart rate. The proposed method is tested using the ECG-Fitness dataset, characterized by challenging environmental conditions such as significant subject motion and dim lighting conditions. Experimental results demonstrate the method’s capability for real-time applications, though further enhancements are needed to improve robustness under difficult environmental conditions.