Today many strategies of monitoring health status and well-being are done through measurementmethods that are connected to the body, e.g. sensors or electrodes. These are often complicatedand requires personal assistance in order to use, because of advanced hardware and attachmentissues. This paper proposes a new method of making it possible for a user to self-monitoring theirwell-being and health status over time by using a non-contact camera system. The camera systemextracts physiological parameters (e.g. Heart Rate (HR), Respiration Rate (RR), Inter-bit-Interval(IBI)) based on facial color variations, due to blood circulation in facial skin. By examining anindividual’s physiological parameters, one can extract measurements that can be used in order tomonitor their well-being. The measurements used in this paper is features of heart rate variability(HRV) that are calculated from the physiological parameter IBI. The HRV features included andtested in this paper is SDNN, RMSSD, NN50 and pNN50 from Time Domain and VLF, LF andLF/HF from Frequency Domain. Machine Learning classification is done in order to classifyan individual’s sleepiness from the given features. The Machine Learning classification modelwhich gave the best results, in forms of accuracy, were Support Vector Machines (SVM). The bestmean accuracy achieved was 84,16% for the training set and 81,67% for the test set for sleepinessdetection with SVM. This paper has great potential for personal health care monitoring and can befurther extended to detect other factors that could help a user to monitor their well-being, such asmeasuring stress level