Feature extraction and learning approaches for cancellable biometrics: A survey
Abstract Biometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-02-01
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| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12283 |
| _version_ | 1797311446878519296 |
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| author | Wencheng Yang Song Wang Jiankun Hu Xiaohui Tao Yan Li |
| author_facet | Wencheng Yang Song Wang Jiankun Hu Xiaohui Tao Yan Li |
| author_sort | Wencheng Yang |
| collection | DOAJ |
| description | Abstract Biometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique for protecting biometric data. Regarding recognition accuracy, feature representation plays a significant role in the performance and reliability of cancellable biometric systems. How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community, especially from researchers of cancellable biometrics. Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance, while the privacy of biometric data is protected. This survey informs the progress, trend and challenges of feature extraction and learning for cancellable biometrics, thus shedding light on the latest developments and future research of this area. |
| first_indexed | 2024-03-08T01:59:46Z |
| format | Article |
| id | doaj.art-6609cce1d5ff4313a9de12623392f7bd |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-03-08T01:59:46Z |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-6609cce1d5ff4313a9de12623392f7bd2024-02-14T05:37:37ZengWileyCAAI Transactions on Intelligence Technology2468-23222024-02-019142510.1049/cit2.12283Feature extraction and learning approaches for cancellable biometrics: A surveyWencheng Yang0Song Wang1Jiankun Hu2Xiaohui Tao3Yan Li4School of Mathematics, Physics and Computing University of Southern Queensland Toowoomba Queensland AustraliaSchool of Computing, Engineering and Mathematical Sciences La Trobe University Melbourne Victoria AustraliaSchool of Engineering and Information Technology University of New South Wales at the Australian Defence Force Academy (UNSW@ADFA) Canberra Australian Capital Territory AustraliaSchool of Mathematics, Physics and Computing University of Southern Queensland Toowoomba Queensland AustraliaSchool of Mathematics, Physics and Computing University of Southern Queensland Toowoomba Queensland AustraliaAbstract Biometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique for protecting biometric data. Regarding recognition accuracy, feature representation plays a significant role in the performance and reliability of cancellable biometric systems. How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community, especially from researchers of cancellable biometrics. Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance, while the privacy of biometric data is protected. This survey informs the progress, trend and challenges of feature extraction and learning for cancellable biometrics, thus shedding light on the latest developments and future research of this area.https://doi.org/10.1049/cit2.12283biometricsfeature extraction |
| spellingShingle | Wencheng Yang Song Wang Jiankun Hu Xiaohui Tao Yan Li Feature extraction and learning approaches for cancellable biometrics: A survey CAAI Transactions on Intelligence Technology biometrics feature extraction |
| title | Feature extraction and learning approaches for cancellable biometrics: A survey |
| title_full | Feature extraction and learning approaches for cancellable biometrics: A survey |
| title_fullStr | Feature extraction and learning approaches for cancellable biometrics: A survey |
| title_full_unstemmed | Feature extraction and learning approaches for cancellable biometrics: A survey |
| title_short | Feature extraction and learning approaches for cancellable biometrics: A survey |
| title_sort | feature extraction and learning approaches for cancellable biometrics a survey |
| topic | biometrics feature extraction |
| url | https://doi.org/10.1049/cit2.12283 |
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