Identity statement area
Reference TypeConference Proceedings
Last Update2018: administrator
Metadata Last Update2020: administrator
Citation KeyAndrezzaPrBoSiBaGo:2018:NoFiQu
TitleA Novel Fingerprint Quality Assessment Based on Gabor Filters
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size870 KiB
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Author1 Andrezza, Igor Lucena Peixoto
2 Primo, João Janduy Brasileiro
3 Borges, Erick Vagner Cabral de Lima
4 Silva, Arnaldo Gualberto de Andrade e
5 Batista, Leonardo Vidal
6 Gomes, Herman Martins
Affiliation1 Vsoft
2 Universidade Federal de Campina Grande
3 Vsoft
4 Vsoft
5 Universidade Federal da Paraiba
6 Universidade Federal de Campina Grande
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2018-08-31 14:11:22 :: -> administrator ::
2020-02-19 03:10:44 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Content TypeExternal Contribution
Tertiary TypeFull Paper
KeywordsBiometrics, Fingerprint, Quality, Gabor.
AbstractFingerprints are the most widely deployed biometric characteristics. However, the recognition of a fingerprint may be influenced by a lot of factors (e.g., skin conditions, sensor conditions) and a matching algorithm is highly affected by the quality of the images involved. This work proposes a novel method for Fingerprint Quality Assessment (FQA) based on the analysis of the Gabor filters response on a fingerprint image. The correlation between the worst quality templates and the matching score has also been analyzed. The method is validated on FVC2000DB3, FVC2004DB2, FVC2004DB3, and FVC2006DB3 databases. This work was compared to other FQAs in order to evaluate performance and with different matching algorithms for fair comparison. The results found pointed that the proposed method is able to identify the images which most affect the error rates of an AFIS, better than the other methods presented in the literature.
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Target FilePID5547019.pdf
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Next Higher Units8JMKD3MGPAW/3RPADUS
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