Human vital signs are essential information that are closely related to both physical cardiac assessments and psychological emotion studies. One of the most important data is the heart rate, which is closely connected to the clinical state of the human body. Modern image processing technologies, such as Remote Photoplethysmography (rPPG), have enabled us to collect and extract the heart rate data from the body by just using an optical sensor and not making any physical contact. In this paper, we propose a real-time camera-based heart rate detector system using computer vision and signal processing techniques. The software of the system is designed to be compatible with both an ordinary built-in color webcam and an industry grade grayscale camera. In addition, we conduct an analysis based on the experimental results collected from a combination of test subjects varying in genders, races, and ages, followed by a quick performance comparison between the color webcam and an industry grayscale camera. The final calculations on percentage error have shown interesting results as the built-in color webcam with the digital spatial filter and the grayscale camera with optical filter achieved relatively similar accuracy under both still and exercising conditions. However, the correlation calculations, on the other hand, have shown that compared to the webcam, the industry grade camera is superior in stability when facial artifacts are presented.