Fingerprint multiple-class classifier: performance evaluation on known and unknown fingerprint spoofing materials

• Published in: International Journal of Biometrics
• Main Author: Senanu Ametefe D.; Seroja Sarnin S.; Mohd Ali D.; John D.B.; Aliu A.A.
• Format: Article
• Language: English
• Published: Inderscience Publishers 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187163389&doi=10.1504%2fIJBM.2024.137088&partnerID=40&md5=60c3905353a3fe79bbdf115e35c7631b

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. Copyright © 2024 Inderscience Enterprises Ltd.