A processor implemented method comprising: dividing a face video of interest into intervals of pre-defined length; computing heart rate 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 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 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 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 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.