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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mostafa, T.M. El-Azab, I.A. El-Gayar, N.F. |
| Copyright Year | 2012 |
| Description | Author affiliation: Faculty of Computers and Information, Cairo University, Egypt (Mostafa, T.M.; El-Azab, I.A.; El-Gayar, N.F.) |
| Abstract | The performance of a biometric verification system may degrade substantially if the input samples vary significantly compared to existing samples in the gallery. Adaptive biometric systems that can improve with use, have recently gained popularity together with using semi-supervised learning methods for accommodating the continuous change in the subject's data. In this study we investigate the value of using quality measures of biometrics to incorporate it in a semi-supervised learning context for the design of an adaptive biometric system. The novelty of the proposed approach is the use of quality information of input samples as an extra source of information to update the user gallery. Our results show that using quality measures in the fusion process will improve system performance. |
| Starting Page | 1313 |
| Ending Page | 1318 |
| File Size | 982135 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467303811 |
| e-ISBN | 9781467303828 |
| e-ISBN | 9781467303804 |
| DOI | 10.1109/ISSPA.2012.6310496 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-07-02 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fingerprint recognition Face Biometrics (access control) Adaptive systems Databases Feature extraction Support vector machine classification |
| Content Type | Text |
| Resource Type | Article |
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