Human recognition from photoplethysmography (PPG) based on non-fiducial features

Abstract

Photoplethysmography (PPG) signals have unique identity properties for human recognition, and are becoming easier to capture by emerging IoT sensors. Existing research on PPG-based biometric systems rely on fiducial methods that extract landmarks from the PPG signal as features. This paper investigates non-fiducial methods that operating in a holistic manner that is less sensitive to noise in landmarks. We compare PPG-based human verification of 42 subjects with fiducial and non-fiducial methods (specifically, discrete wavelet transform) and classification using a neural network and support vector machine. The experimental results demonstrate higher test recognition rates for wavelet transform feature extraction. We further improve our results by selecting a subset of features via the genetic algorithm.