Robust photoplethysmographic (PPG) based biometric authentication for wireless body area networks and m-health applications

In this paper, we present noise-robust photoplethysmographic (PPG) based biometric authentication method for wireless body area networks and m-health applications. The method consists of four steps: (i) preprocessing of PPG signals, (ii) systolic peak detection, (iii) ensemble averaged pulsatile waveform extraction and (iv) pulsatile waveform similarity matching using a normalized cross correlation (NCC) measure. The performance of the proposed method is tested and validated using different types of PPG signals taken from the standard PPG databases. For predefined threshold of 0.997, the NCC-based PPG biometric method achieves an average false rejection rate (FRR) of 0.32 and false acceptance rate (FAR) of 0.32. Performance evaluation results show that the proposed method achieves consistent authentication results as compared to the other methods under different kinds of artifacts and noise.