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XDU-Chongqing IC Innovation Research Institute","award":["CQIRI-2022CXY-Y14"],"award-info":[{"award-number":["CQIRI-2022CXY-Y14"]}]},{"name":"Cooperation Program of XDU-Chongqing IC Innovation Research Institute","award":["LGF20F010006"],"award-info":[{"award-number":["LGF20F010006"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["61876139"],"award-info":[{"award-number":["61876139"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["cstc2021jcyj-msxmX1068"],"award-info":[{"award-number":["cstc2021jcyj-msxmX1068"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["21RGSF0011"],"award-info":[{"award-number":["21RGSF0011"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["CQIRI-2022CXY-Y14"],"award-info":[{"award-number":["CQIRI-2022CXY-Y14"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LGF20F010006"],"award-info":[{"award-number":["LGF20F010006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the rapid development of fingerprint recognition systems, fingerprint liveness detection is gradually becoming regarded as the main countermeasure to protect the fingerprint identification system from spoofing attacks. Convolutional neural networks have shown great potential in fingerprint liveness detection. However, the generalization ability of the deep network model for unknown materials, and the computational complexity of the network, need to be further improved. A new lightweight fingerprint liveness detection network is here proposed to distinguish fake fingerprints from real ones. The method includes mainly foreground extraction, fingerprint image blocking, style transfer based on CycleGan and an improved ResNet with multi-head self-attention mechanism. The proposed method can effectively extract ROI and obtain the end-to-end data structure, which increases the amount of data. For false fingerprints generated from unknown materials, the use of CycleGan network improves the model generalization ability. The introduction of Transformer with MHSA in the improved ResNet improves detection performance and reduces computing overhead. Experiments on the LivDet2011, LivDet2013 and LivDet2015 datasets showed that the proposed method achieves good results. For example, on the LivDet2015 dataset, our methods achieved an average classification error of 1.72 across all sensors, while significantly reducing network parameters, and the overall parameter number was only 0.83 M. At the same time, the experiment on small-area fingerprints yielded an accuracy of 95.27%.<\/jats:p>","DOI":"10.3390\/s23156854","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T09:32:35Z","timestamp":1690882355000},"page":"6854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["LFLDNet: Lightweight Fingerprint Liveness Detection Based on ResNet and Transformer"],"prefix":"10.3390","volume":"23","author":[{"given":"Kang","family":"Zhang","sequence":"first","affiliation":[{"name":"Engineering Research Centre of Molecular & Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Huang","sequence":"additional","affiliation":[{"name":"Engineering Research Centre of Molecular & Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eryun","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Information Network Technology, College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0242-7609","authenticated-orcid":false,"given":"Heng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Engineering Research Centre of Molecular & Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Marcel, S., Nixon, M.S., and Li, S.Z. 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