{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:08:01Z","timestamp":1760238481479,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T00:00:00Z","timestamp":1597017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671328"],"award-info":[{"award-number":["61671328"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Partially defective fingerprint image (PDFI) with poor performance poses challenges to the automated fingerprint identification system (AFIS). To improve the quality and the performance rate of PDFI, it is essential to use accurate segmentation. Currently, most fingerprint image segmentations use methods with ridge orientation, ridge frequency, coherence, variance, local gradient, etc. This paper proposes a method of XFinger-Net for segmenting PDFIs. Based on U-Net, XFinger-Net inherits its characteristics. The attention gate with fewer parameters is used to replace the cascaded network, which can suppress uncorrelated regions of PDFIs. Moreover, the XFinger-Net implements a pixel-level segmentation and takes non-blocking fingerprint images as an input to preserve the global characteristics of PDFIs. The XFinger-Net can achieve a very good segmentation effect as demonstrated in the self-made fingerprint segmentation test.<\/jats:p>","DOI":"10.3390\/s20164473","type":"journal-article","created":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T09:28:57Z","timestamp":1597138137000},"page":"4473","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net"],"prefix":"10.3390","volume":"20","author":[{"given":"Guo Chun","family":"Wan","sequence":"first","affiliation":[{"name":"Department of Electronic Science and Technology, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0620-8852","authenticated-orcid":false,"given":"Meng Meng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic Science and Technology, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Electronic Science and Technology, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen Hao","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Electronic Science and Technology, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin Wen","family":"Rui","sequence":"additional","affiliation":[{"name":"Sino-German College of Applied Science, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mei Song","family":"Tong","sequence":"additional","affiliation":[{"name":"Department of Electronic Science and Technology, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Maltoni, D., Maio, D., Jain, A.K., and Prabhakar, S. 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