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A Unified Model for Fingerprint Authentication and Presentation Attack Detection

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Handbook of Biometric Anti-Spoofing

Abstract

Typical fingerprint recognition systems are comprised of a spoof detection module and a subsequent recognition module, running one after the other. In this study, we reformulate the workings of a typical fingerprint recognition system. We show that both spoof detection and fingerprint recognition are correlated tasks, and therefore, rather than performing these two tasks separately, a joint model capable of performing both spoof detection and matching simultaneously can be used without compromising the accuracy of either task. We demonstrate the capability of our joint model to obtain an authentication accuracy (1:1 matching) of TAR \(=\) 100% @ FAR \(=\) 0.1% on the FVC 2006 DB2A dataset while achieving a spoof detection ACE of 1.44% on the LivDet 2015 dataset, both maintaining the performance of stand-alone methods. In practice, this reduces the time and memory requirements of the fingerprint recognition system by 50 and 40%, respectively; a significant advantage for recognition systems running on resource constrained devices and communication channels.

Additya Popli and Saraansh Tandon—These authors have contributed equally.

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Notes

  1. 1.

    The terms authentication, verification and matching have been used interchangeably to refer to a 1:1 match surpassing a threshold.

  2. 2.

    We have used a student model since the data used in the original paper is not publicly available and the authors of [7] agreed to share the weights of their model to use it as a teacher network.

  3. 3.

    https://github.com/prip-lab/MSU-LatentAFIS.

  4. 4.

    We ignore the time required for the minutiae matching since it is common to both the traditional and proposed pipeline and takes only \(\sim \)1 ms.

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Correspondence to Additya Popli , Saraansh Tandon or Anoop Namboodiri .

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Popli, A., Tandon, S., Engelsma, J.J., Namboodiri, A. (2023). A Unified Model for Fingerprint Authentication and Presentation Attack Detection. In: Marcel, S., Fierrez, J., Evans, N. (eds) Handbook of Biometric Anti-Spoofing. Advances in Computer Vision and Pattern Recognition. Springer, Singapore. https://doi.org/10.1007/978-981-19-5288-3_4

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  • DOI: https://doi.org/10.1007/978-981-19-5288-3_4

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