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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2022

12.07.2022

Presentation Attack Detection Using Referential Quality Metrics and Minutiae Count

verfasst von: Akhilesh Verma, Anshdha Gupta, Mohammad Akbar, Arun Kumar Yadav, Divakar Yadav

Erschienen in: Wireless Personal Communications | Ausgabe 4/2022

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Abstract

The fingerprint presentation attack is still a major challenge in biometric systems because of its increased applications worldwide. In the past, researchers used fingerprint presentation attack detection (FPAD) for user authentication, but it suffers from reliable authentication due to less focus on reducing the ‘error rate’. In this paper, we proposed an algorithm, based on referential image quality-metrics and minutiae count using neural network, k-NN and SVM for FPAD. We evaluate and validate the error rate reduction with different machine learning models on the public domain, such as LivDet crossmatch dataset 2015 and achieved an accuracy of 88% with a neural network, 88.6% with k-NN and 88.8% using SVM. In addition, the average classification error (ACE) score is 0.1197 for ANN, 0.1138 for k-NN and 0.1117 for SVM. Thus, the results obtained show that it was achieved a reasonable accuracy with a low ACE score with respect to other state-of-the-art methods.
Vorheriger Artikel A Wake-up Radio-Based Energy-Efficient Multi-Hop Clustering Protocol for WSNs
Nächster Artikel Design of Hybrid Precoding for Millimeter-Wave Massive MIMO System

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Metadaten
Titel
Presentation Attack Detection Using Referential Quality Metrics and Minutiae Count
verfasst von
Akhilesh Verma
Anshdha Gupta
Mohammad Akbar
Arun Kumar Yadav
Divakar Yadav
Publikationsdatum
12.07.2022
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2022
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-022-09921-6

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