Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis
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. How...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/11/3279 |
| _version_ | 1797565786539163648 |
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| author | Ching-Yao Chou Yo-Woei Pua Ting-Wei Sun An-Yeu (Andy) Wu |
| author_facet | Ching-Yao Chou Yo-Woei Pua Ting-Wei Sun An-Yeu (Andy) Wu |
| author_sort | Ching-Yao Chou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:17:55Z |
| format | Article |
| id | doaj.art-688770f523db45f99e5f22cb02bf4e42 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:17:55Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-688770f523db45f99e5f22cb02bf4e422023-11-20T03:15:33ZengMDPI AGSensors1424-82202020-06-012011327910.3390/s20113279Compressed-Domain ECG-Based Biometric User Identification Using Compressive AnalysisChing-Yao Chou0Yo-Woei Pua1Ting-Wei Sun2An-Yeu (Andy) Wu3Department of Electrical Engineering, Graduate Institute of Electronics Engineering, National Taiwan University, Taipei 106, TaiwanDepartment of Electrical Engineering, Graduate Institute of Electronics Engineering, National Taiwan University, Taipei 106, TaiwanDepartment of Electrical Engineering, Graduate Institute of Electronics Engineering, National Taiwan University, Taipei 106, TaiwanDepartment of Electrical Engineering, Graduate Institute of Electronics Engineering, National Taiwan University, Taipei 106, TaiwanNowadays, 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.https://www.mdpi.com/1424-8220/20/11/3279user identificationECG biometriccompressive sensingcompressive analysisECG signal alignment |
| spellingShingle | Ching-Yao Chou Yo-Woei Pua Ting-Wei Sun An-Yeu (Andy) Wu Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis Sensors user identification ECG biometric compressive sensing compressive analysis ECG signal alignment |
| title | Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis |
| title_full | Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis |
| title_fullStr | Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis |
| title_full_unstemmed | Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis |
| title_short | Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis |
| title_sort | compressed domain ecg based biometric user identification using compressive analysis |
| topic | user identification ECG biometric compressive sensing compressive analysis ECG signal alignment |
| url | https://www.mdpi.com/1424-8220/20/11/3279 |
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