A method for estimating humanly vital signs by using a remote non-invasive photo-plethysmography measurement system and wherein an active appearance model is used to model a face and track facial landmark points such that captured facial color values are replaced with a parametric appearance model vector wherein the said method comprises the steps of; • grabbing a new frame 110 either from a previously recorded video or an online monitoring system that captures images in a real time setup, • performing a face detection method 200 before the actual facial modelling 210, • resetting 300 the whole analysis if there is no face detected in the frame, • Utilizing active shape and appearance based facial modelling 210, • obtaining the model of the face for a given head pose and facial expression via facial landmark points generated by active appearance modelling, • using the detected 3D facial landmark points to parameterize the shape and appearance of the face in the current frame, • in the currently captured frame, if a region of interest definition is available from the previously captured frame, conducting a search for the same region in the current frame, • resetting the analysis and capturing a new frame if the region of interest is not available, • in a first detrending step, subtracting from the raw color signal s(t), the mean of the color signal in a selected time window at a data normalization step in order to remove the DC part of the signal, further normalizing the signal to (-1,1) for analysis, thus obtaining a detrended raw signal D(s(t)), • removing noise by subtracting the detrended raw signal Ds(t) from the raw signal s(t) for obtaining a signal s(t) - D(s(t)), and in a second detrending step, detrending the obtained signal s(t) - D(s(t)) to remove the remaining aperiodic trend in the signal, • extracting the periodic signal information from the twice detrended signal via a time 260 and frequency 270 domain analysis, • merging the two independent analyses at a final step 280 for quality analysis by means of integrating the time and frequency domain measurements wherein, a. the time domain confidence is estimated using the magnitude and variation of heart rate peaks, and b. the frequency domain confidence is dependent on the peak frequency magnitude with respect to the remaining frequency components • using a confidence voting between the time and frequency domain estimation before publishing the final analysis results if the two analyses do not agree, • capturing the next frame upon the end of the analysis steps. • grabbing a new frame 110 either from a previously recorded video or an online monitoring system that captures images in a real time setup, • performing a face detection method 200 before the actual facial modelling 210, • resetting 300 the whole analysis if there is no face detected in the frame, • Utilizing active shape and appearance based facial modelling 210, • obtaining the model of the face for a given head pose and facial expression via facial landmark points generated by active appearance modelling, • using the detected 3D facial landmark points to parameterize the shape and appearance of the face in the current frame, • in the currently captured frame, if a region of interest definition is available from the previously captured frame, conducting a search for the same region in the current frame, • resetting the analysis and capturing a new frame if the region of interest is not available, • in a first detrending step, subtracting from the raw color signal s(t), the mean of the color signal in a selected time window at a data normalization step in order to remove the DC part of the signal, further normalizing the signal to (-1,1) for analysis, thus obtaining a detrended raw signal D(s(t)), • removing noise by subtracting the detrended raw signal Ds(t) from the raw signal s(t) for obtaining a signal s(t) - D(s(t)), and in a second detrending step, detrending the obtained signal s(t) - D(s(t)) to remove the remaining aperiodic trend in the signal, • extracting the periodic signal information from the twice detrended signal via a time 260 and frequency 270 domain analysis, • merging the two independent analyses at a final step 280 for quality analysis by means of integrating the time and frequency domain measurements wherein, a. the time domain confidence is estimated using the magnitude and variation of heart rate peaks, and b. the frequency domain confidence is dependent on the peak frequency magnitude with respect to the remaining frequency components a. the time domain confidence is estimated using the magnitude and variation of heart rate peaks, and b. the frequency domain confidence is dependent on the peak frequency magnitude with respect to the remaining frequency components • using a confidence voting between the time and frequency domain estimation before publishing the final analysis results if the two analyses do not agree, • capturing the next frame upon the end of the analysis steps.