Abstract
Fingerprint recognition systems have played a significant role in the field of biometric security in recent years. However, it is vulnerable to several threats which can put the biometric security system at a significant risk. Presentation attack or spoofing is one of these attacks which utilizes a fake fingerprint created with a fabrication material by an intruder to fool the authentication system. Development of new fabrication materials makes this spoof detection more challenging for cross materials. In this work, we have proposed a novel approach for detecting these presentation attacks using Auxiliary Classifier-Generative Adversarial Networks (AC-GAN). The performance of the proposed method is assessed in an open set paradigm on publicly available LivDet Competition 2013 and 2015 datasets. Proposed methodology achieves an average accuracy of \(98.52 \%\) and \(92.02 \%\) on the LivDet 2013 and LivDet 2015 datasets, respectively which outperforms the state-of-the-art methods.
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Anshul, A., Jha, A., Jain, P., Rai, A., Sharma, R.P., Dey, S. (2024). An Enhanced Generative Adversarial Network Model for Fingerprint Presentation Attack Detection. In: Ghosh, A., King, I., Bhattacharyya, M., Sankar Ray, S., K. Pal, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2021. Lecture Notes in Computer Science, vol 13102. Springer, Cham. https://doi.org/10.1007/978-3-031-12700-7_39
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