| Autor: |
Ametefe, Divine Senanu, Sarnin, Suzi Seroja, Ali, Darmawaty Mohd, John, Dah B., Aliu, Abdulmalik Adozuka |
| Zdroj: |
International Journal of Biometrics; 2024, Vol. 16 Issue: 2 p113-132, 20p |
| Abstrakt: |
Fingerprint recognition is a popular and reliable biometric technology used in many security-sensitive applications. However, the use of fake fingerprints made from ubiquitous spoofing materials poses a significant threat to security systems. While several studies have proposed binary classifiers to detect fingerprint presentation attacks, relatively few have explored the effectiveness of multiple-class classifiers in detecting known and unknown spoofs. In this study, we evaluated the efficacy of multiple-class classifiers using deep transfer learning to detect presentation attacks made with different spoofing materials. Our experiments on the LivDet 2009-2015 datasets showed that while a classifier model developed without data augmentation performed better on known spoofs, it showed poor performance on cross-material detection of all seven fingerprint spoofing materials. These results suggest that modelling a multiple-class classifier is not an efficient approach for detecting cross-material presentation attacks in fingerprint recognition systems. |
| Databáze: |
Supplemental Index |
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