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Reference TypeConference Proceedings
Identifier8JMKD3MGPBW34M/3EEQDE8
Repositorysid.inpe.br/sibgrapi/2013/07.12.20.34
Metadatasid.inpe.br/sibgrapi/2013/07.12.20.34.53
Sitesibgrapi.sid.inpe.br
Citation KeyWangRamoFierKris:2013:ToReFu
Author1 Wang, Ruifang
2 Ramos, Daniel
3 Fierrez, Julian
4 Krish, Ram P.
Affiliation1 Universidad Autonoma de Madrid
2 Universidad Autonoma de Madrid
3 Universidad Autonoma de Madrid
4 Universidad Autonoma de Madrid
TitleTowards Regional Fusion for High-Resolution Palmprint Recognition
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
Year2013
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
Book TitleProceedings
DateAug. 5-8, 2013
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationArequipa, Peru
KeywordsHigh resolution palmprints, regional fusion.
AbstractThe existing high resolution palmprint matching algorithms essentially follow the minutiae-based fingerprint matching strategy and focus on full-to-full/partial-to-full palmprint comparison. These algorithms would face problems when they are applied to forensic palmprint recognition where latent marks have much smaller area than full palmprints. Therefore, towards forensic scenarios, we propose a novel matching strategy based on regional fusion for high resolution palmprint recognition using regions segmented by major creases features. The matching strategy includes two stages: 1) region-to-region palmprint comparison; 2) regional fusion at score level. We first studied regional discriminability of a high resolution palmprint under the concept of three regions, i.e., interdigital, hypothenar and thenar, which is the most significant difference between palmprits and fingerprints. Then we implemented regional fusion based on logistic regression at score level using region-to-region comparison scores obtained by a commercial SDK, MegaMatcher 4.0. Significant improvement of recognition accuracy is achieved by regional fusion on a public high resolution palmprint database THUPALMLAB. The EER of logistic regression based regional fusion is 0.25%, while the EER of full-to-full palmprint comparison is 1%.
Languageen
Tertiary TypeFull Paper
FormatOn-line.
Size1590 KiB
Number of Files1
Target FileCamera_Ready_Towards Regional Fusion for High Resolution Palmprint Recognition.pdf
Last Update2013:07.12.20.34.53 sid.inpe.br/banon/2001/03.30.15.38 ruifang.wang@uam.es
Metadata Last Update2020:02.19.03.09.22 sid.inpe.br/banon/2001/03.30.15.38 administrator {D 2013}
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Is the master or a copy?is the master
Mirrorsid.inpe.br/banon/2001/03.30.15.38.24
e-Mail Addressruifang.wang@uam.es
User Groupruifang.wang@uam.es
Visibilityshown
Transferable1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Content TypeExternal Contribution
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History2013-07-12 20:34:53 :: ruifang.wang@uam.es -> administrator ::
2020-02-19 03:09:22 :: administrator -> :: 2013
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
Access Date2020, Nov. 25

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