https://scholars.lib.ntu.edu.tw/handle/123456789/611212
標題: | Compressed-domain ECG-based biometric user identification using compressive analysis | 作者: | AN-YEU(ANDY) WU | 關鍵字: | Compressive analysis; Compressive sensing; ECG biometric; ECG signal alignment; User identification | 公開日期: | 2020 | 卷: | 20 | 期: | 11 | 起(迄)頁: | 1-19 | 來源出版物: | Sensors (Switzerland) | 摘要: | 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 further enhance the precision of identifications and reduce the total training time. The experimental result shows that our proposed algorithm has a 94.16% accuracy based on a public database of 22 people. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086307335&doi=10.3390%2fs20113279&partnerID=40&md5=96eef5121c59e838eb48e90bac4e8f6a https://scholars.lib.ntu.edu.tw/handle/123456789/611212 |
ISSN: | 14248220 | DOI: | 10.3390/s20113279 | SDG/關鍵字: | Biometrics; Compressed sensing; Data acquisition; Electrocardiography; Biological traits; Biometric methods; Compressed domain; Compressive sensing; Data acquisition and transmissions; Extract informations; Physiological signals; User identification; Biomedical signal processing; algorithm; biometry; electrocardiography; human; information processing; Algorithms; Biometric Identification; Data Compression; Electrocardiography; Humans |
顯示於: | 電機工程學系 |
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