A Non-Contact PPG Biometric System Based on Deep Neural Network

Omkar R. Patil, Wei Wang, Yang Gao, Wenyao Xu, Zhanpeng Jin

Abstract: The objective of this study is to develop a non-contact biometric system with photoplethysmogram (PPG). A novel method for non-contact PPG acquisition based on the Laplacian pyramid is proposed in this paper with the authentication module based on the deep neural network (DNN). Laplacian pyramid based video amplification technique extracts the subtle changes of blood volume as a result of the cardiovascular activities in the facial region. The video data was recorded from 20 subjects in varying light conditions at different places, resembling different scenarios in the generalized environment. Authentication accuracy ranges from 66.67% to 100% with an average of 86.67%. In order to validate the repeatability of PPG waveforms, a comparative analysis of the correlation coefficients for the waveforms recorded over a month are conducted.