Logo des Repositoriums
 

Biometric Recognition in a Multi-sample Multi-Subject Facial Image Database: The 1:M:N System Model

Kurzanzeige
dc.contributor.authorHalfen, DeWayne
dc.contributor.authorRajaraman, Srinivasan
dc.contributor.authorWayman, James L.
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDamer, Naser
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorGomez-Barrero, Marta
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorSequeira, Ana
dc.contributor.editorUhl, Andreas
dc.date.accessioned2021-10-04T08:43:47Z
dc.date.available2021-10-04T08:43:47Z
dc.date.issued2021
dc.description.abstractOver the last 50 years, biometric recognition has advanced from localized “identity verification” applications [GU77][RY74] to include large-scale systems in which “a determination is made as to the identity of an individual independently of any information supplied by the individual” [GU77]. Models for estimating and expressing system error rates (both false matches and false non-matches) have been largely limited to so-called “1-to-1” and “1-to-N” systems in which each identity is represented by only one enrolled reference [Gr21]. In this paper, we create a highly simplified simulation model for a common current situation in which each known identity record has multiple stored references. We call this the “1:M:N” model and show that both DET and CMC performance depend upon the number of identities and images per identity, not simply the total number of references images, as usually assumed. Although trialed here on very simple decision policies, this model will be extended in future work to more complex decision criteria.en
dc.identifier.isbn978-3-88579-709-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37458
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-321
dc.subjectBiometric System Modeling
dc.subjectLarge-scale systems
dc.subjectDetection Error Trade-off curve
dc.subjectCumulative Match Characteristics
dc.titleBiometric Recognition in a Multi-sample Multi-Subject Facial Image Database: The 1:M:N System Modelen
dc.typeText/Conference Paper
gi.citation.endPage228
gi.citation.publisherPlaceBonn
gi.citation.startPage221
gi.conference.date15.-17. September 2021
gi.conference.locationInternational Digital Conference
gi.conference.sessiontitleFurther Conference Contributions

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild
Name:
biosig2021_proceedings_23.pdf
Größe:
431.42 KB
Format:
Adobe Portable Document Format
Herunterladen

Sammlungen

P315 - BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group