Photoplethysmographic Subject Identification by Considering Feature Values Derived from Heartbeat and Respiration

This research proposes a subject identification method using PPG (Photoplethysmogram) signals towards continuous authentication. The proposed method uses feature values derived from heartbeat and respiration extracted from PPG signals by means of frequency filtering and MFCC (Mel-Frequency Cepstrum Coefficients) to identify subjects. An experiment was conducted using an open dataset containing PPG signals to investigate the identification performance of the method. The feature values were extracted from the PPG signals and classifiers were generated to evaluate the performance of the method. As a result, the proposed method was found to be capable of identifying 46 people with the accuracy of 92.9 % by using feature values derived from heartbeat and respiration.