Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition  

作　　者：Chun-Ying Liu Gong-Ping Yang Yu-Wen Huang Fu-Xian Huang 

机构地区：[1]School of computer,Heze University,Heze 274015,China [2]School of Software,Shandong University,Jinan 250101,China

出　　处：《Machine Intelligence Research》2023年第5期707-715,共9页机器智能研究（英文版）

基　　金：supported by National Nature Science Foundation of China(No.62276093);in part by Natural Science Foundation of Shandong Province,China(No.2022MF86).

摘　　要：Photoplethysmography(PPG)biometrics have received considerable attention.Although deep learning has achieved good performance for PPG biometrics,several challenges remain open:1)How to effectively extract the feature fusion representation from time and frequency PPG signals.2)How to effectively capture a series of PPG signal transition information.3)How to extract timevarying information from one-dimensional time-frequency sequential data.To address these challenges,we propose a dual-domain and multiscale fusion deep neural network(DMFDNN)for PPG biometric recognition.The DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics,which can learn the time-varying and multiscale discriminative features from the time and frequency domains.Meanwhile,we design a multiscale extraction module to capture transition information,which consists of multiple convolution layers with different receptive fields for capturing multiscale transition information.In addition,the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG biometrics.Experiments on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.

关 键 词：Photoplethysmography(PPG)signal biometric recognition multiple scale deep neural network dual-domain attention. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术] R318[医药卫生—生物医学工程]