| dc.contributor.author | Medvedev, V | |
| dc.contributor.author | Gonçalves, Nuno | |
| dc.contributor.author | Cruz, Leandro | |
| dc.contributor.editor | Brömme, Arslan | |
| dc.contributor.editor | Busch, Christoph | |
| dc.contributor.editor | Dantcheva, Antitza | |
| dc.contributor.editor | Raja, Kiran | |
| dc.contributor.editor | Rathgeb, Christian | |
| dc.contributor.editor | Uhl, Andreas | |
| dc.date.accessioned | 2020-09-16T08:25:50Z | |
| dc.date.available | 2020-09-16T08:25:50Z | |
| dc.date.issued | 2020 | |
| dc.identifier.isbn | 978-3-88579-700-5 | |
| dc.identifier.issn | 1617-5468 | |
| dc.identifier.uri | http://dl.gi.de/handle/20.500.12116/34346 | |
| dc.description.abstract | Current trends in security of ID and travel documents require portable and efficient validation
applications that rely on biometric recognition. Such tools can allow any authority and citizen to
validate documents and authenticate citizens with no need of expensive and sometimes unavailable
proprietary devices. In this work, we present a novel, compact and efficient approach of validating ID
and travel documents for offline mobile applications. The approach employs the in-house biometric
template that is extracted from the original portrait photo (either full frontal or token frontal), and
then stored on the ID document with use of a machine readable code (MRC). The ID document can
then be validated with a developed application on a mobile device with digital camera. The similarity
score is estimated with use of an artificial neural network (ANN). Results show that we achieve
validation accuracy up to 99.5% with corresponding false match rate = 0.0047 and false non-match
rate = 0.00034. | en |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-306 | |
| dc.subject | Document security | |
| dc.subject | biometric template | |
| dc.subject | active appearance model | |
| dc.subject | artificial neural network. | |
| dc.title | Biometric System for Mobile Validation of ID And Travel Documents | en |
| dc.type | Text/Conference Paper | |
| dc.pubPlace | Bonn | |
| mci.reference.pages | 67-76 | |
| mci.conference.sessiontitle | Regular Research Papers | |
| mci.conference.location | International Digital Conference | |
| mci.conference.date | 16.-18. September 2020 | |