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Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach

Liveness detection for fingerprint impressions plays a role in the meaningful prevention of any unauthorized activity or phishing attempt. The accessibility of unique individual identification has increased the popularity of biometrics. Deep learning with computer vision has proven remarkable result...

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Autores principales: Kothadiya, Deep, Bhatt, Chintan, Soni, Dhruvil, Gadhe, Kalpita, Patel, Samir, Bruno, Alessandro, Mazzeo, Pier Luigi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10455454/
https://www.ncbi.nlm.nih.gov/pubmed/37623690
http://dx.doi.org/10.3390/jimaging9080158
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author Kothadiya, Deep
Bhatt, Chintan
Soni, Dhruvil
Gadhe, Kalpita
Patel, Samir
Bruno, Alessandro
Mazzeo, Pier Luigi
author_facet Kothadiya, Deep
Bhatt, Chintan
Soni, Dhruvil
Gadhe, Kalpita
Patel, Samir
Bruno, Alessandro
Mazzeo, Pier Luigi
author_sort Kothadiya, Deep
collection PubMed
description Liveness detection for fingerprint impressions plays a role in the meaningful prevention of any unauthorized activity or phishing attempt. The accessibility of unique individual identification has increased the popularity of biometrics. Deep learning with computer vision has proven remarkable results in image classification, detection, and many others. The proposed methodology relies on an attention model and ResNet convolutions. Spatial attention (SA) and channel attention (CA) models were used sequentially to enhance feature learning. A three-fold sequential attention model is used along with five convolution learning layers. The method’s performances have been tested across different pooling strategies, such as Max, Average, and Stochastic, over the LivDet-2021 dataset. Comparisons against different state-of-the-art variants of Convolutional Neural Networks, such as DenseNet121, VGG19, InceptionV3, and conventional ResNet50, have been carried out. In particular, tests have been aimed at assessing ResNet34 and ResNet50 models on feature extraction by further enhancing the sequential attention model. A Multilayer Perceptron (MLP) classifier used alongside a fully connected layer returns the ultimate prediction of the entire stack. Finally, the proposed method is also evaluated on feature extraction with and without attention models for ResNet and considering different pooling strategies.
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spelling pubmed-104554542023-08-26 Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach Kothadiya, Deep Bhatt, Chintan Soni, Dhruvil Gadhe, Kalpita Patel, Samir Bruno, Alessandro Mazzeo, Pier Luigi J Imaging Article Liveness detection for fingerprint impressions plays a role in the meaningful prevention of any unauthorized activity or phishing attempt. The accessibility of unique individual identification has increased the popularity of biometrics. Deep learning with computer vision has proven remarkable results in image classification, detection, and many others. The proposed methodology relies on an attention model and ResNet convolutions. Spatial attention (SA) and channel attention (CA) models were used sequentially to enhance feature learning. A three-fold sequential attention model is used along with five convolution learning layers. The method’s performances have been tested across different pooling strategies, such as Max, Average, and Stochastic, over the LivDet-2021 dataset. Comparisons against different state-of-the-art variants of Convolutional Neural Networks, such as DenseNet121, VGG19, InceptionV3, and conventional ResNet50, have been carried out. In particular, tests have been aimed at assessing ResNet34 and ResNet50 models on feature extraction by further enhancing the sequential attention model. A Multilayer Perceptron (MLP) classifier used alongside a fully connected layer returns the ultimate prediction of the entire stack. Finally, the proposed method is also evaluated on feature extraction with and without attention models for ResNet and considering different pooling strategies. MDPI 2023-08-07 /pmc/articles/PMC10455454/ /pubmed/37623690 http://dx.doi.org/10.3390/jimaging9080158 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kothadiya, Deep
Bhatt, Chintan
Soni, Dhruvil
Gadhe, Kalpita
Patel, Samir
Bruno, Alessandro
Mazzeo, Pier Luigi
Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach
title Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach
title_full Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach
title_fullStr Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach
title_full_unstemmed Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach
title_short Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach
title_sort enhancing fingerprint liveness detection accuracy using deep learning: a comprehensive study and novel approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10455454/
https://www.ncbi.nlm.nih.gov/pubmed/37623690
http://dx.doi.org/10.3390/jimaging9080158
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