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		<citationkey>SáLotu:2006:ImFiMa</citationkey>
		<title>Improved FingerCode Matching Function</title>
		<format>On-line</format>
		<year>2006</year>
		<date>8-11 Oct. 2006</date>
		<numberoffiles>1</numberoffiles>
		<size>323 KiB</size>
		<author>de Sá, Gustavo Ferreira Cradoso,</author>
		<author>Lotufo, Roberto de Alencar,</author>
		<affiliation>Universidade Estadual de Campinas</affiliation>
		<affiliation>Unversidade Estadual de Campinas</affiliation>
		<editor>Oliveira Neto, Manuel Menezes de,</editor>
		<editor>Carceroni, Rodrigo Lima,</editor>
		<e-mailaddress>gustavosa@uol.com.br</e-mailaddress>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)</conferencename>
		<conferencelocation>Manaus</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<tertiarytype>Full Paper</tertiarytype>
		<keywords>Fingerprint, FingerCode.</keywords>
		<abstract>FingerCode is a fingerprint correlation matching scheme that relies on texture information. In this scheme, the oriented components are extracted from a fingerprint image using a bank of Gabor filters, and a directional texture feature vector is computed for each oriented component. The feature vectors from the input and template images are compared and a matching score is obtained. Here we explore ways to improve the matching score for the FingerCode method by using more complex matching functions. The best results were obtained by applying a nonlinear function to the texture values and weighting the texture vectors based on the spatial distribution.</abstract>
		<language>en</language>
		<targetfile>PID282231.pdf</targetfile>
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