Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis.

Sensors
Ching-Yao ChouAn-Yeu Andy Wu

Abstract

Nowadays, user identification plays a more and more important role for authorized machine access and remote personal data usage. For reasons of privacy and convenience, biometrics-based user identification, such as iris, fingerprint, and face ID, has become mainstream methods in our daily lives. However, most of the biometric methods can be easily imitated or artificially cracked. New types of biometrics, such as electrocardiography (ECG), are based on physiological signals rather than traditional biological traits. Recently, compressive sensing (CS) technology that combines both sampling and compression has been widely applied to reduce the power of data acquisition and transmission. However, prior CS-based frameworks suffer from high reconstruction overhead and cannot directly align compressed ECG signals. In this paper, in order to solve the above two problems, we propose a compressed alignment-aided compressive analysis (CA-CA) algorithm for ECG-based biometric user identification. With CA-CA, it can avoid reconstruction and extract information directly from CS-based compressed ECG signals to reduce overall complexity and power. Besides, CA-CA can also align the compressed ECG signals in the eigenspace-domain, which can fur...Continue Reading

References

Apr 3, 2003·IEEE Transactions on Bio-medical Engineering·Eric LaciarDana H Brooks
May 25, 2011·IEEE Transactions on Bio-medical Engineering·Hossein MamaghanianPierre Vandergheynst
May 4, 2013·Journal of Medical Systems·Farid Touati, Rohan Tabish
Apr 1, 2012·IEEE Transactions on Biomedical Circuits and Systems·Anna M R DixonDavid J Allstot
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Kaihua ZhangMing-Hsuan Yang
Jul 12, 2018·IEEE Transactions on Biomedical Circuits and Systems·Ching-Yao ChouAn-Yeu Wu

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Methods Mentioned

BETA
feature extraction
PCA

Software Mentioned

MOD
CA
Off

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