A processor implemented method comprising: dividing a face video of interest into intervals of pre-defined length ; computing heart rate (HR) for each of the intervals based on temporal signals obtained by Eulerian approach ; determining a first poorness measure based on heart rate variability in consecutive time intervals for temporal signals obtained by the Eulerian approach ; computing heart rate based on Lagrangian approach, if the first poorness measure is greater than a pre-defined threshold ; determining a second poorness measure based on heart rate variability in consecutive time intervals for temporal signals obtained by the Lagrangian approach , wherein the step of determining the first poorness measure and the step of determining the second poorness measure comprises: normalizing the temporal signals in an interval; maximizing Kurtosis associated with the normalized temporal signal to extract photoplethysmogram (PPG) signal; transforming the PPG signal to frequency domain; computing the heart rate based on frequency corresponding to maximum amplitude associated with the PPG signal; and computing the first poorness measure or the second poorness measure as a function of the heart rate variability in consecutive time intervals in heart rate in beats per minute (bpm); and identifying, in real-time, heart rate associated with lesser of the first poorness measure and the second poorness measure as the heart rate for a corresponding face in the face video . dividing a face video of interest into intervals of pre-defined length ; computing heart rate (HR) for each of the intervals based on temporal signals obtained by Eulerian approach ; determining a first poorness measure based on heart rate variability in consecutive time intervals for temporal signals obtained by the Eulerian approach ; computing heart rate based on Lagrangian approach, if the first poorness measure is greater than a pre-defined threshold ; determining a second poorness measure based on heart rate variability in consecutive time intervals for temporal signals obtained by the Lagrangian approach , wherein the step of determining the first poorness measure and the step of determining the second poorness measure comprises: normalizing the temporal signals in an interval; maximizing Kurtosis associated with the normalized temporal signal to extract photoplethysmogram (PPG) signal; transforming the PPG signal to frequency domain; computing the heart rate based on frequency corresponding to maximum amplitude associated with the PPG signal; and computing the first poorness measure or the second poorness measure as a function of the heart rate variability in consecutive time intervals in heart rate in beats per minute (bpm); and identifying, in real-time, heart rate associated with lesser of the first poorness measure and the second poorness measure as the heart rate for a corresponding face in the face video . normalizing the temporal signals in an interval; maximizing Kurtosis associated with the normalized temporal signal to extract photoplethysmogram (PPG) signal; transforming the PPG signal to frequency domain; computing the heart rate based on frequency corresponding to maximum amplitude associated with the PPG signal; and computing the first poorness measure or the second poorness measure as a function of the heart rate variability in consecutive time intervals in heart rate in beats per minute (bpm); and identifying, in real-time, heart rate associated with lesser of the first poorness measure and the second poorness measure as the heart rate for a corresponding face in the face video . <EOS>