{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T11:17:45Z","timestamp":1781522265163,"version":"3.54.1"},"reference-count":27,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:00:00Z","timestamp":1621814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006566","name":"National Plan for Science,Technology and Innovation","doi-asserted-by":"publisher","award":["13-INF946-02"],"award-info":[{"award-number":["13-INF946-02"]}],"id":[{"id":"10.13039\/501100006566","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The fingerprint is one of the leading biometric modalities that is used worldwide for authenticating the identity of persons. Over time, a lot of research has been conducted to develop automatic fingerprint verification techniques. However, due to different authentication needs, the use of different sensors and the fingerprint verification systems encounter cross-sensor matching or sensor interoperability challenges, where different sensors are used for the enrollment and query phases. The challenge is to develop an efficient, robust and automatic system for cross-sensor matching. This paper proposes a new cross-matching system (SiameseFinger) using the Siamese network that takes the features extracted using the Gabor-HoG descriptor. The proposed Siamese network is trained using adversarial learning. The SiameseFinger was evaluated on two benchmark public datasets FingerPass and MOLF. The results of the experiments presented in this paper indicate that SiameseFinger achieves a comparable performance with that of the state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s21113657","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T23:35:05Z","timestamp":1621899305000},"page":"3657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Cross-Sensor Fingerprint Matching Using Siamese Network and Adversarial Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5516-3865","authenticated-orcid":false,"given":"Adhwa","family":"Alrashidi","sequence":"first","affiliation":[{"name":"Department of Computer Science, CCIS, King Saud University, Riyadh 11451, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7476-9862","authenticated-orcid":false,"given":"Ashwaq","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, CCIS, King Saud University, Riyadh 11451, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5847-8539","authenticated-orcid":false,"given":"Muhammad","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Computer Science, CCIS, King Saud University, Riyadh 11451, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helala","family":"AlShehri","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, Jubail University College, Al Jubail 35716, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8000-5105","authenticated-orcid":false,"given":"Hatim A.","family":"AboAlSamh","sequence":"additional","affiliation":[{"name":"Department of Computer Science, CCIS, King Saud University, Riyadh 11451, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Bebis","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1007\/978-3-540-25976-3_13","article-title":"Biometric sensor interoperability: A case study in fingerprints","volume":"3087","author":"Ross","year":"2004","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"28951","DOI":"10.1109\/ACCESS.2018.2840330","article-title":"Cross-Sensor Fingerprint Matching Method Based on Orientation, Gradient, and Gabor-HOG Descriptors with Score Level Fusion","volume":"6","author":"Alshehri","year":"2018","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alonso-fernandez, F., Veldhuis, R.N.J., Bazen, A.M., Fierrez-Aguilar, J., and Ortega-Garcia, J. (2006, January 5\u20138). Sensor Interoperability and Fusion in Fingerprint Verification: A Case Study using Minutiae- and Ridge-Based Matchers. Proceedings of the 9th International Conference on Control, Automation, Robotics and Vision, Singapore.","DOI":"10.1109\/ICARCV.2006.345483"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"86436","DOI":"10.1109\/ACCESS.2019.2924127","article-title":"Alignment-Free Cross-Sensor Fingerprint Matching Based on the Co-Occurrence of Ridge Orientations and Gabor-HOG Descriptor","volume":"7","author":"Alshehri","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1109\/TKDE.2007.190696","article-title":"A thin-plate spline calibration model for fingerprint sensor interoperability","volume":"20","author":"Ross","year":"2008","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.patcog.2003.12.021","article-title":"A deformable model for fingerprint matching","volume":"38","author":"Ross","year":"2005","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zang, Y., Yang, X., Jia, X., Zhang, N., Tian, J., and Zhu, X. (2013, January 4\u20137). A Coarse-fine Fingerprint Scaling Method. Proceedings of the 2013 International Conference on Biometrics (ICB), Madrid, Spain.","DOI":"10.1109\/ICB.2013.6612986"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zang, Y., Yang, X., Jia, X., Zhang, N., Tian, J., and Zhao, J. (2013, January 4\u20137). Evaluation of minutia cylinder-code on fingerprint cross-matching and its improvement with scale. Proceedings of the 2013 International Conference on Biometrics (ICB), Madrid, Spain.","DOI":"10.1109\/ICB.2013.6613005"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"908","DOI":"10.4028\/www.scientific.net\/AMM.303-306.908","article-title":"Fingerprint scaling for sensor interoperability","volume":"303","author":"Guo","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1109\/TIFS.2018.2854765","article-title":"A CNN-Based Framework for Comparison of Contactless to Contact-Based Fingerprints","volume":"14","author":"Lin","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lin, C., and Kumar, A. (2017, January 1\u20134). Multi-Siamese networks to accurately match contactless to contact-based fingerprint images. Proceedings of the 2017 IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA.","DOI":"10.1109\/BTAS.2017.8272708"},{"key":"ref_12","unstructured":"Jia, X., Yang, X., Zang, Y., Zhang, N., and Tian, J. (2012, January 11\u201315). A cross-device matching fingerprint database from multi-type sensors. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1109\/ACCESS.2015.2428631","article-title":"Multisensor Optical and Latent Fingerprint Database","volume":"3","author":"Sankaran","year":"2015","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12414","DOI":"10.1016\/j.eswa.2009.04.041","article-title":"Descriptors for image-based fingerprint matchers","volume":"36","author":"Nanni","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1109\/34.709565","article-title":"Fingerprint image enhancement: Algorithm and performance evaluation","volume":"20","author":"Hong","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lugini, I.G.L., Marasco, E., and Cukic, B. (2013, January 24\u201327). Interoperability in Fingerprint Recognition: A Large-Scale Empirical Study. Proceedings of the 2013 43rd Annual IEEE\/IFIP Conference on Dependable Systems and Networks Workshop (DSN-W), Budapest, Hungary.","DOI":"10.1109\/DSNW.2013.6615516"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mason, S., Gashi, I., Lugini, L., Marasco, E., and Cukic, B. (2014, January 23\u201326). Interoperability between Fingerprint Biometric Systems: An Empirical Study. Proceedings of the 2014 44th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks, Atlanta, GA, USA.","DOI":"10.1109\/DSN.2014.60"},{"key":"ref_18","unstructured":"Rathod, P., Nangre, M., and Kolhale, P. (2020). Contactless Fingerprint Recognition System Based On CNN. Int. J. Future Gener. Commun. Netw., 1373\u20131379."},{"key":"ref_19","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_21","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_22","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. Generative adversarial nets. Proceedings of the NIPS\u201914: Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, QC, Canada, 8\u201313 December 2014."},{"key":"ref_23","unstructured":"Ganin, Y., and Lempitsky, V. (2015, January 6\u201311). Unsupervised Domain Adaptation by Backpropagation. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_24","first-page":"265","article-title":"Evaluation of biometric systems: A study of users\u2019 acceptance and satisfaction","volume":"4","author":"Charrier","year":"2012","journal-title":"Int. J. Biom."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"735","DOI":"10.3844\/jcssp.2006.735.739","article-title":"Data Mining: A Preprocessing Engine","volume":"2","author":"Shalabi","year":"2006","journal-title":"J. Comput. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2128","DOI":"10.1109\/TPAMI.2010.52","article-title":"Minutia Cylinder-Code: A new representation and matching technique for fingerprint recognition","volume":"32","author":"Cappelli","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","unstructured":"(2016, September 30). VeriFinger SDK 9. Available online: http\/\/www.neurotechnology.com\/verifinger.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/11\/3657\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:07:02Z","timestamp":1760162822000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/11\/3657"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,24]]},"references-count":27,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["s21113657"],"URL":"https:\/\/doi.org\/10.3390\/s21113657","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,24]]}}}