As the current world approaches a better lifestyle, medical problems have also increased and gained a lot of attention in the last couple of years. Among others, heart rate detection has become a vital part of remote healthcare monitoring and diagnostic systems. The contactless heart rate measurement is a recent concept that monitors the colour changes in the skin, specifically the facial region, to assess the heart rate of the human. In this paper, the authors have presented an analysis of various machine learning classifiers and independent component analysis methods to support this concept, as the naked eye visualisation cannot detect minor changes in the face. Hence, machine learning and magnification of the frame become a vital step. The paper describes the general procedure of detecting the heart rate by monitoring the forehead of a live object. The training and classification procedure is discussed in detail, and a comparative analysis based on quantitative parameters for different machine learning classifiers is also presented in this paper. The analysis among various classifiers shows that the neural network with the highest accuracy proved to be a better machine learning classifier for heart rate detection work.