A Review of ECG Biometrics: Generalization in Deep Learning with Attention Mechanisms
Common biometric systems like fingerprint and face recognition are more convenient in daily applications, but biological and behavioral characteristics of the biometrics features can be fabricated and digitally stolen. Thus, biometrics features with liveness detection such as the electrocardiogram (...
| الحاوية / القاعدة: | Lecture Notes in Electrical Engineering |
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| المؤلف الرئيسي: | |
| التنسيق: | Conference paper |
| اللغة: | English |
| منشور في: |
Springer Science and Business Media Deutschland GmbH
2024
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| الوصول للمادة أونلاين: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190385476&doi=10.1007%2f978-981-99-9005-4_57&partnerID=40&md5=8707407e04af68edbb028ce329e321e3 |
| id |
Saod A.H.M.; Ramli D.A. |
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Saod A.H.M.; Ramli D.A. 2-s2.0-85190385476 A Review of ECG Biometrics: Generalization in Deep Learning with Attention Mechanisms 2024 Lecture Notes in Electrical Engineering 1123 LNEE 10.1007/978-981-99-9005-4_57 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190385476&doi=10.1007%2f978-981-99-9005-4_57&partnerID=40&md5=8707407e04af68edbb028ce329e321e3 Common biometric systems like fingerprint and face recognition are more convenient in daily applications, but biological and behavioral characteristics of the biometrics features can be fabricated and digitally stolen. Thus, biometrics features with liveness detection such as the electrocardiogram (ECG) have been introduced as its features are hidden and difficult to forge. This study presents a review of ECG biometrics based on deep learning and generalization issues in deep learning. Based on the review, deep learning methods such as recurrent neural networks (RNN) and long short-term memory networks (LSTM) with attention mechanisms can be employed to improve the performance and generalization ability of ECG biometrics systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Springer Science and Business Media Deutschland GmbH 18761100 English Conference paper |
| author |
2-s2.0-85190385476 |
| spellingShingle |
2-s2.0-85190385476 A Review of ECG Biometrics: Generalization in Deep Learning with Attention Mechanisms |
| author_facet |
2-s2.0-85190385476 |
| author_sort |
2-s2.0-85190385476 |
| title |
A Review of ECG Biometrics: Generalization in Deep Learning with Attention Mechanisms |
| title_short |
A Review of ECG Biometrics: Generalization in Deep Learning with Attention Mechanisms |
| title_full |
A Review of ECG Biometrics: Generalization in Deep Learning with Attention Mechanisms |
| title_fullStr |
A Review of ECG Biometrics: Generalization in Deep Learning with Attention Mechanisms |
| title_full_unstemmed |
A Review of ECG Biometrics: Generalization in Deep Learning with Attention Mechanisms |
| title_sort |
A Review of ECG Biometrics: Generalization in Deep Learning with Attention Mechanisms |
| publishDate |
2024 |
| container_title |
Lecture Notes in Electrical Engineering |
| container_volume |
1123 LNEE |
| container_issue |
|
| doi_str_mv |
10.1007/978-981-99-9005-4_57 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190385476&doi=10.1007%2f978-981-99-9005-4_57&partnerID=40&md5=8707407e04af68edbb028ce329e321e3 |
| description |
Common biometric systems like fingerprint and face recognition are more convenient in daily applications, but biological and behavioral characteristics of the biometrics features can be fabricated and digitally stolen. Thus, biometrics features with liveness detection such as the electrocardiogram (ECG) have been introduced as its features are hidden and difficult to forge. This study presents a review of ECG biometrics based on deep learning and generalization issues in deep learning. Based on the review, deep learning methods such as recurrent neural networks (RNN) and long short-term memory networks (LSTM) with attention mechanisms can be employed to improve the performance and generalization ability of ECG biometrics systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| issn |
18761100 |
| language |
English |
| format |
Conference paper |
| accesstype |
|
| record_format |
scopus |
| collection |
Scopus |
| _version_ |
1828987863312629760 |