Abstract
In the modern computerized biometric authentication system, a fingerprint spoof detector is utilized to differentiate the real and fake human finger. The efficiency of spoof detectors will be enhanced by introducing more number of testing and training set of images. When the spoof detector is exposed to bogus images that are not part of the training set, the performance of any such spoof detector will degrade. To address the security threat posed by new spoof attacks, this paper proposes a Weibull-calibrated SVM (W-SVM) approach for recognizing the robustness in the new material used in spoof generation detection and a method to detect how the interoperability across the classifiers gets automatically adapted to new spoof materials. Experiments have been conducted with new segments of the database, which are built for spoofs composed of new materials and later evaluated with existing spoof detectors. It was discovered that while testing with the existing method, the rate of error increases; however, when the recommended adaptive approach was used, the spoof detection and performance gets improved significantly.
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Kanmani Pappa, C., Kavitha, T., Rama Krishna, I., Venkata Lokesh, V., Narayana, A.V.L. (2023). Generalization of Fingerprint Spoof Detector. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Izonin, I. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-6004-8_14
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DOI: https://doi.org/10.1007/978-981-19-6004-8_14
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