{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:43:42Z","timestamp":1777704222324,"version":"3.51.4"},"reference-count":16,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T00:00:00Z","timestamp":1577664000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,4,30]]},"abstract":"<jats:p>With the development of technology, fingerprint identification has become an effective means of personal identification, and has been widely used in the fields of public security, custom, banking, network security and other areas requiring identification. Nowadays, many effective methods have been proposed for fingerprint identification, but these methods are not effective in identifying damaged fingerprints, and the correct recognition rate is low. In order to effectively solve the problem of identification and classification of damaged fingerprints, this paper proposes a method for classification of broken fingerprints based on deep learning fuzzy theory. Firstly, after pre-processing the fingerprint, using the bifurcation point and the endpoint in the broken fingerprint image as the minutiae, the feature extraction ability of the deep convolutional neural network is utilized to extract the feature of the damaged fingerprint minutiae. Secondly, the fuzzy rough set is used to reduce the feature. Finally, using the reduced feature uses the Softmax classifier to classify the damaged fingerprint image. The simulation results show that, after preprocessing the damaged fingerprint image, using OPTA algorithm to refine the damaged fingerprint image, the features of the fingerprint image can be extracted effectively by deep convolutional neural network, and then the classification accuracy can be improved by using Softmax classifier to reduce the features.<\/jats:p>","DOI":"10.3233\/jifs-179575","type":"journal-article","created":{"date-parts":[[2019,12,31]],"date-time":"2019-12-31T07:39:16Z","timestamp":1577777956000},"page":"3529-3537","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Recognition and classification of damaged fingerprint based on deep learning fuzzy theory"],"prefix":"10.1177","volume":"38","author":[{"given":"Xinfeng","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"},{"name":"School of Computer and Information Engineering, Nanyang Institute of Technology, Nanyang, China"}]},{"given":"Qiping","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}]},{"given":"Shuaihao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}]}],"member":"179","published-online":{"date-parts":[[2019,12,30]]},"reference":[{"issue":"3","key":"e_1_3_2_2_2","first-page":"262","article-title":"Application of fingerprint identification technology in surveillance of HIV-infected Myanmar patients in Dehong Prefecture, 2014\u20132015","volume":"51","author":"Li L.","year":"2017","unstructured":"LiL., YangY.C. and TangR.H., Application of fingerprint identification technology in surveillance of HIV-infected Myanmar patients in Dehong Prefecture, 2014\u20132015, Zhonghua Yu Fang Yi Xue Za Zhi 51(3) (2017), 262\u2013264.","journal-title":"Zhonghua Yu Fang Yi Xue Za Zhi"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3035968"},{"issue":"21","key":"e_1_3_2_4_2","first-page":"216","article-title":"Origin traceability of Heilongjiang soybean using fingerprint of mineral elements","volume":"33","author":"Lu B.","year":"2017","unstructured":"LuB. and ZhangD., Origin traceability of Heilongjiang soybean using fingerprint of mineral elements, Transactions of the Chinese Society of Agricultural Engineering 33(21) (2017), 216\u2013221.","journal-title":"Transactions of the Chinese Society of Agricultural Engineering"},{"issue":"3","key":"e_1_3_2_5_2","first-page":"3642","article-title":"Identification and verification of differentially expressed microRNAs and their target genes for the diagnosis of esophageal cancer","volume":"16","author":"Cai X.","year":"2018","unstructured":"CaiX., YangX. and JinC., Identification and verification of differentially expressed microRNAs and their target genes for the diagnosis of esophageal cancer, Oncology Letters 16(3) (2018), 3642\u20133650.","journal-title":"Oncology Letters"},{"issue":"2","key":"e_1_3_2_6_2","first-page":"117","article-title":"Identification and verification of potential piRNAs from domesticated yak testis","volume":"155","author":"Gong J.","year":"2018","unstructured":"GongJ., ZhangQ. and WangQ., Identification and verification of potential piRNAs from domesticated yak testis, Reproduction 155(2) (2018), 117\u2013127.","journal-title":"Reproduction"},{"issue":"1","key":"e_1_3_2_7_2","first-page":"260","article-title":"Based on image corrosion trees segmentation method research and simulation","volume":"29","author":"Zhang Y.B.","year":"2012","unstructured":"ZhangY.B., Based on image corrosion trees segmentation method research and simulation, Computer Simulation 29(1) (2012), 260\u2013263.","journal-title":"Computer Simulation"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/2566590.2566610"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2713099"},{"key":"e_1_3_2_10_2","doi-asserted-by":"crossref","unstructured":"XiaoT. 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