Spatio-temporal filtering of thermal video sequences for heart rate estimation

In this paper, a novel method for non-contact measurement of heart rate using thermal imaging was proposed. Thermal videos were recorded from subjects' faces. The measurements are performed on three different areas: the whole face, the upper half of the face and the supraorbital region. A tracker was used to track these regions to make the algorithm invulnerable to the subject's motion. After tracking, the videos were spatially filtered using a full Laplacian pyramid decomposition to increase the signal to noise ratio; next, the video frames were successively temporally filtered using an ideal bandpass filter for extracting the thermal variations caused by blood circulation. Finally, the heart rate was calculated by using two methods including zero crossing and Fast Fourier Transform. For evaluating the results, the complement of absolute normalized difference (CAND) index was used which was introduced by Pavlidis. This index was 99.42% in the best case and 92.472% in average for 22 subjects. These results showed a growth in CAND index in comparison with previous work. Zerocrossing outperformed FFT because of the nonstationary nature of thermal signals. Another benefit of our method is that, the videos are taken from the face unlike most of the studies that take it from the neck and Carotid. Neck and carotid are less accessible than faces. Finally, the optimum ROI for estimating the heart rate from face was identified. (C) 2016 Elsevier Ltd. All rights reserved.