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dc.contributor.author김회율-
dc.date.accessioned2019-12-08T03:48:23Z-
dc.date.available2019-12-08T03:48:23Z-
dc.date.issued2018-05-
dc.identifier.citationSYMMETRY-BASEL, v. 10, no. 5, Article no. 175en_US
dc.identifier.issn2073-8994-
dc.identifier.urihttps://www.mdpi.com/2073-8994/10/5/175-
dc.identifier.urihttp://repository.hanyang.ac.kr/handle/20.500.11754/118733-
dc.description.abstractSweat pores on the human fingertip have meaningful patterns that enable individual identification. Although conventional automatic fingerprint identification systems (AFIS) have mainly employed the minutiae features to match fingerprints, there has been minimal research that uses sweat pores to match fingerprints. Recently, high-resolution optical sensors and pore-based fingerprint systems have become available, which motivates research on pore analysis. However, most existing pore-based AFIS methods use the minutia-ridge information and image pixel distribution, which limit their applications. In this context, this paper presents a stable pore matching algorithm which effectively removes both the minutia-ridge and fingerprint-device dependencies. Experimental results show that the proposed pore matching algorithm is more accurate for general fingerprint images and robust under noisy conditions compared with existing methods. The proposed method can be used to improve the performance of AFIS combined with the conventional minutiae-based methods. Since sweat pores can also be observed using various systems, removing of the fingerprint-device dependency will make the pore-based AFIS useful for wide applications including forensic science, which matches the latent fingerprint to the fingerprint image in databases.en_US
dc.description.sponsorshipThis work was supported by the Chung-Ang University Excellent Student Scholarship in 2016 and by the Institute for Information and communications Technology Promotion (IITP) grant funded by the Korean government(MSIT) (2017-0-00250, Intelligent Defense Boundary Surveillance Technology Using Collaborative Reinforced Learning of Embedded Edge Camera and Image Analysis). Without support for the PolyU HRF database, this research would have been diminished.en_US
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.subjectbiometric identificationen_US
dc.subjectfingerprint recognitionen_US
dc.subjectsweat pore matchingen_US
dc.subjectbipartite graph matchingen_US
dc.subjectstable marriage problemen_US
dc.titleOptimum Geometric Transformation and Bipartite Graph-Based Approach to Sweat Pore Matching for Biometric Identificationen_US
dc.typeArticleen_US
dc.relation.no5-
dc.relation.volume10-
dc.identifier.doi10.3390/sym10050175-
dc.relation.page1-3-
dc.relation.journalSYMMETRY-BASEL-
dc.contributor.googleauthorKim, Min-jae-
dc.contributor.googleauthorKim, Whoi-Yul-
dc.contributor.googleauthorPaik, Joonki-
dc.relation.code2018008485-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF ELECTRONIC ENGINEERING-
dc.identifier.pidwykim-


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