Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2010:
Metadata Last Update2010:
Citation KeyPaulinoJainFeng:2010:FuMaMa
TitleLatent fingerprint matching: fusion of manually marked and derived minutiae
FormatPrinted, On-line.
Access Date2021, Jan. 25
Number of Files1
Size2285 KiB
Context area
Author1 Paulino, Alessandra Aparecida
2 Jain, Anil K.
3 Feng, Jianjiang
Affiliation1 Michigan State University
2 Michigan State University
3 Tsinghua University
EditorBellon, Olga
Esperanša, Claudio
Conference NameConference on Graphics, Patterns and Images, 23 (SIBGRAPI)
Conference LocationGramado
DateAug. 30 - Sep. 3, 2010
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2010-10-01 04:19:39 :: -> :: 2010
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordslatent fingerprint, fingerprint matching, rolled fingerprint, enhancement, minutiae extraction, interoperability.
AbstractMatching unknown latent fingerprints lifted from crime scenes to full (rolled or plain) fingerprints in law enforcement databases is of critical importance for combating crime and fighting terrorism. Compared to good quality full fingerprints acquired using live-scan or inking methods during enrollment, latent fingerprints are often smudgy and blurred, capture only a small finger area, and have large nonlinear distortion. For this reason, features (minutiae and singular points) in latents are typically manually marked by trained latent examiners. However, this introduces an undesired interoperability problem between latent examiners and automatic fingerprint identification systems (AFIS); the features marked by examiners are not always compatible with those automatically extracted by AFIS, resulting in reduced matching accuracy. While the use of automatically extracted minutiae from latents can avoid interoperability problem, such minutiae tend to be very unreliable, because of the poor quality of latents. In this paper, we improve latent to full fingerprint matching accuracy by combining manually marked (ground truth) minutiae with automatically extracted minutiae. Experimental results on a public domain database, NIST SD27, demonstrate the effectiveness of the proposed algorithm.
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