{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T22:20:20Z","timestamp":1788906020886,"version":"build-2803163510"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/jimaging9080158","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T12:35:42Z","timestamp":1691498142000},"page":"158","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5997-1720","authenticated-orcid":false,"given":"Deep","family":"Kothadiya","sequence":"first","affiliation":[{"name":"U & P U Patel Department of Computer Engineering, CHA-RUSAT Campus, Charotar University of Science and Technology, Petlad 388421, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0423-0159","authenticated-orcid":false,"given":"Chintan","family":"Bhatt","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhruvil","family":"Soni","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalpita","family":"Gadhe","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4280-6446","authenticated-orcid":false,"given":"Samir","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0707-6131","authenticated-orcid":false,"given":"Alessandro","family":"Bruno","sequence":"additional","affiliation":[{"name":"Department of Business, Economics, Law, Consumer Behaviour, IULM University, 20143 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7552-2394","authenticated-orcid":false,"given":"Pier Luigi","family":"Mazzeo","sequence":"additional","affiliation":[{"name":"ISASI Institute of Applied Sciences & Intelligent Systems-CNR, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Priesnitz, J., Huesmann, R., Rathgeb, C., Buchmann, N., and Busch, C. 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