@inproceedings{6be4b5a79630452f9db9db11e00d6973,
title = "A non-contact PPG biometric system based on deep neural network",
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.",
author = "Patil, \{Omkar R.\} and Wei Wang and Yang Gao and Wenyao Xu and Zhanpeng Jin",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS 2018 ; Conference date: 22-10-2018 Through 25-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/BTAS.2018.8698552",
language = "英语",
series = "2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018",
address = "美国",
}