A2IPPG: A Machine Learning Assisted Real-Time Standalone Continuous Authorization to Identification Using Photoplethysmogram Features

Continuous biometric authentication employs several security measures, which endorse the legitimate user in a continuous manner with consideration of issues such as security, safety, and convenience. However, the reported state-of-the-art works put less focus on the validation of authentication model with unseen unauthorized users and hardware implementation. In this letter, a real-time 2-stage photoplethysmogram (PPG) assisted continuous authentication technique is presented with legitimate user authorization (anomaly detection) followed by identification. A denoised short segment of 5 s PPG signal is used to extract time, frequency, entropy, and statistical features followed by recursive feature elimination, XGBOD, and RF-based authorization and identification, respectively. The proposed technique was tested with PhysioNet BIDMC PPG and Respiration dataset (53 subjects) and laboratory collected dataset (10 subjects) to detect authorized users and unauthorized-unseen users with overall accuracy and F1 score of 96.62% and 96.63%, respectively, for real-time standalone implementation. The end-to-end model was deployed in a BCM2837B0, Cortex-A53 64-bit SoC (1.4 GHz processor, 1 GB RAM) with a latency of ∼8.038 s and memory consumption of ∼49.891 MB for 5 s PPG data. The present technique may be extended to further resource constrained environment with increased number of authorizations.