{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T05:08:18Z","timestamp":1769317698555,"version":"3.49.0"},"reference-count":26,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,13]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Fingerprint recognition is the most widely used identification method at present. However, it still falls short in terms of cross-platform and algorithmic complexity, which exerts a certain effect on the migration of fingerprint data and the development of the system. The conventional image recognition methods require offline standard databases constructed in advance for image access efficiency. The database can provide a pre-processed image via a specific method that probably is compatible merely with the specific recognition algorithm. Then, the specific recognition algorithm starts the process of retrieving these specific pre-proessing images for recognition and inevitably will be blocked from other datasets. The proposed method in this research designed an embedded image processing algorithm based on a Siamese neural network in the recognition method that allows the proposed method to recognize images from any source without constructing a database for image storage in advance. In this research, the proposed method was applied to fingerprint recognition and evaluation of the proposed method was evaluated. The results showed that the accuracy of the proposed algorithm was up to 92%, and its<jats:italic>F<\/jats:italic>1 score was up to 0.87. Compared with the conventional fingerprint matching methods, its significant advantage in the FRR, FAR, and CR jointly indicated the remarkable correct recognition rate of the proposed method.<\/jats:p>","DOI":"10.1515\/jisys-2022-0055","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T14:06:49Z","timestamp":1655129209000},"page":"690-705","source":"Crossref","is-referenced-by-count":21,"title":["A novel fingerprint recognition method based on a Siamese neural network"],"prefix":"10.1515","volume":"31","author":[{"given":"Zihao","family":"Li","sequence":"first","affiliation":[{"name":"Department of Automation, College of Intelligent Science and Control Engineering, Jinling Institute of Technology , Nanjing , China"}]},{"given":"Yizhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Automation, College of Intelligent Science and Control Engineering, Jinling Institute of Technology , Nanjing , China"}]},{"given":"Zhong","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Automation, College of Intelligent Science and Control Engineering, Jinling Institute of Technology , Nanjing , China"}]},{"given":"Xiaomin","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Automation, College of Intelligent Science and Control Engineering, Jinling Institute of Technology , Nanjing , China"}]},{"given":"Lixin","family":"Zhai","sequence":"additional","affiliation":[{"name":"Department of Automation, College of Intelligent Science and Control Engineering, Jinling Institute of Technology , Nanjing , China"}]},{"given":"Xiao","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Automation, College of Intelligent Science and Control Engineering, Jinling Institute of Technology , Nanjing , China"}]},{"given":"Jianpeng","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Automation, College of Intelligent Science and Control Engineering, Jinling Institute of Technology , Nanjing , China"}]},{"given":"Shanshan","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Automation, College of Intelligent Science and Control Engineering, Jinling Institute of Technology , Nanjing , China"}]},{"given":"Lingyi","family":"Huang","sequence":"additional","affiliation":[{"name":"Deparmtent of Pharmaceutical Analysis, School of Pharmacy, Fujian Medical University , Fuzhou , China"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Polymer Materials, Fujian Key Laboratory of Functional Marine Sensing Materials, College of Materials and Chemcial Engineering , Fuzhou , China"}]}],"member":"374","published-online":{"date-parts":[[2022,6,13]]},"reference":[{"key":"2022120618433918098_j_jisys-2022-0055_ref_001","doi-asserted-by":"crossref","unstructured":"Khademi AF, Zulkernine M, Weldemariam K. An empirical evaluation of web-based fingerprinting. IEEE Softw. 2015;32:46\u201352.","DOI":"10.1109\/MS.2015.77"},{"key":"2022120618433918098_j_jisys-2022-0055_ref_002","unstructured":"Takano A. The history of practical application of fingerprinting: networks of the British Empire and the \u201cproblem\u201d of controlling human mobilities. JAMA Intern Med. 2015;175:257\u201360."},{"key":"2022120618433918098_j_jisys-2022-0055_ref_003","unstructured":"Li X. The past and present of fingerprint identification technology. China: Chinese Government General Services; 2021. p. 64\u20136 [Chinese]."},{"key":"2022120618433918098_j_jisys-2022-0055_ref_004","unstructured":"Luo Y, Guo W., Footprinting Tutorial, People\u2019s Public Security, University of China Press, China; 2010."},{"key":"2022120618433918098_j_jisys-2022-0055_ref_005","doi-asserted-by":"crossref","unstructured":"Krish RP, Fierrez J, Ramos D, Ortega-Garcia J, Bigun J. Pre-registration for improved latent fingerprint identification. Proceedings of International Conference on Pattern Recognition; 2014 Aug 1\u20133. p. 696\u2013701.","DOI":"10.1109\/ICPR.2014.130"},{"key":"2022120618433918098_j_jisys-2022-0055_ref_006","unstructured":"Satheesh KP. Svm-bdt based intelligent fingerprint authentication system using geometry approach. Int J Comput Netw Inf Secur. 2021;4:1."},{"key":"2022120618433918098_j_jisys-2022-0055_ref_007","doi-asserted-by":"crossref","unstructured":"Fang B, Wen H, Liu RZ, Tang YY. A new fingerprint thinning algorithm. Chinese Conference on Pattern Recognition (CCPR); 2010. p. 1\u20134.","DOI":"10.1109\/CCPR.2010.5659273"},{"key":"2022120618433918098_j_jisys-2022-0055_ref_008","unstructured":"Wang S. Overview of fingerprint identification technology. J Inf Secur Res. 2016;2(7):343\u201355 [Chinese]."},{"key":"2022120618433918098_j_jisys-2022-0055_ref_009","doi-asserted-by":"crossref","unstructured":"Wang Z, Li N, Wu T, Zhang H, Feng T. Simulation of human ear recognition sound direction based on convolutional neural network. J Intell Syst. 2021;30(1):209\u201323.","DOI":"10.1515\/jisys-2019-0250"},{"key":"2022120618433918098_j_jisys-2022-0055_ref_010","doi-asserted-by":"crossref","unstructured":"Zhu L, Zhang H, Ali S, Yang B, Li C. Crowd counting via multi-scale adversarial convolutional neural networks. J Intell Syst. 2021;30(1):180\u201391.","DOI":"10.1515\/jisys-2019-0157"},{"key":"2022120618433918098_j_jisys-2022-0055_ref_011","doi-asserted-by":"crossref","unstructured":"Bromle J, Guyon I, LeCun Y, Sackinger E, Shah R. Signature verification using a \u201cSiamese\u201d time delay neural network. Int J Pattern Recognit Artif Intell. 1993;11:737\u201344.","DOI":"10.1142\/S0218001493000339"},{"key":"2022120618433918098_j_jisys-2022-0055_ref_012","doi-asserted-by":"crossref","unstructured":"Kamijo M. Classifying fingerprint images using neural network: deriving the classification state. IEEE International Conference on Neural Networks. Vol. 3, 2002 Aug 6. p. 1932\u20137.","DOI":"10.1109\/ICNN.1993.298852"},{"key":"2022120618433918098_j_jisys-2022-0055_ref_013","doi-asserted-by":"crossref","unstructured":"Mazumdar A, Bora PK. Siamese convolutional neural network-based approach towards universal image forensics. IET Image Process. 2020;14(13):3105\u201316.","DOI":"10.1049\/iet-ipr.2019.1114"},{"key":"2022120618433918098_j_jisys-2022-0055_ref_014","doi-asserted-by":"crossref","unstructured":"Wang L, Lin ZQ, Wong A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. 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Chin Comput Commun. 2011;5:32\u20133 [Chinese]."},{"key":"2022120618433918098_j_jisys-2022-0055_ref_018","unstructured":"Yuan Y, Li L, Yang Y. Fingerprint image recognition algorithm based on FPN-SE-Capsule network. Ind Control Comput. 2021;34:45\u201347 + 50 [Chinese]."},{"key":"2022120618433918098_j_jisys-2022-0055_ref_019","doi-asserted-by":"crossref","unstructured":"Deshpande UU, Malemath VS, Patil SM, Chaugule SV. CNNAI: a convolution neural network-based latent fingerprint matching using the combination of nearest neighbor arrangement indexing. Front Robot AI. 2020;7:113.","DOI":"10.3389\/frobt.2020.00113"},{"key":"2022120618433918098_j_jisys-2022-0055_ref_020","doi-asserted-by":"crossref","unstructured":"Ma ZQ, Sun XX, Cheng MJ, Wang SH. Research on the application of convolutional-deep neural networks in parallel fingerprint minutiae matching. Int J Biometrics. 2021;13:96.","DOI":"10.1504\/IJBM.2021.10034254"},{"key":"2022120618433918098_j_jisys-2022-0055_ref_021","unstructured":"Technicolor T, Related S. ImageNet classification with deep convolutional neural networks. https:\/\/web.cs.ucdavis.edu\/\u223cyjlee\/teaching\/ecs289g-winter2018\/alexnet.pdf (last retrieved 18\/03\/2022)."},{"key":"2022120618433918098_j_jisys-2022-0055_ref_022","unstructured":"Arora S, Bhaskara A, Ge R, Ma T. Provable bounds for learning some deep representations. U.S.A: Cornell Univerisity; 2013. https:\/\/arxiv.org\/pdf\/1310.6343v1.pdf (last retrieved 18\/03\/2022)."},{"key":"2022120618433918098_j_jisys-2022-0055_ref_023","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 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