Unlinkable improved multi-biometric iris fuzzy vault View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-12

AUTHORS

Christian Rathgeb, Benjamin Tams, Johannes Wagner, Christoph Busch

ABSTRACT

Iris recognition technologies are deployed in numerous large-scale nation-wide projects in order to provide robust and reliable biometric recognition of individuals. Moreover, the iris has been found to be rather stable over time, i.e. iris biometric reference data provides a strong and permanent link between individuals and their biometric traits. Hence, unprotected storage of (iris) biometric data provokes serious privacy threats, e.g. identity theft, limited re-newability, or cross-matching. Biometric cryptosystems grant a significant improvement in data privacy and increase the likelihood that individuals will effectively consent in the biometric system usage. However, the vast majority of proposed biometric cryptosystems do not guarantee desired properties of irreversibility, unlinkability, and re-newability without significantly degrading the biometric performance. In this work, we propose an unlinkable multi-instance iris biometric cryptosystem based on the improved fuzzy vault scheme. The proposed system locks biometric feature sets extracted from binary iris biometric reference data, i.e. iris-codes, of the left and right irises in a single fuzzy vault. In order to retain the size of the protected template and authentication speed, the proposed fusion step combines the most discriminative parts of two iris-codes at feature level. It is shown that the proposed key-binding process enables the generation of irreversible protected templates which prevents from previously proposed cross-matching attacks. Further, we investigate the optimal choice among potential decoding strategies with respect to biometric performance and time of key retrieval. The fully reproducible system is integrated to two different publicly available iris recognition systems and evaluated on the CASIAv3-Interval and the IITDv1 iris databases. Compared to the corresponding unprotected recognition schemes, genuine match rates of approximately 95 and 97 % at which no false accepts are observed and maintained in a single- and multi-instance scenario, respectively. Moreover, the multi-iris system is shown to significantly improve privacy protection achieving security levels of approximately 70 bits at practical biometric performance. More... »

PAGES

26

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13635-016-0049-9

DOI

http://dx.doi.org/10.1186/s13635-016-0049-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003756807


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Darmstadt University of Applied Sciences", 
          "id": "https://www.grid.ac/institutes/grid.449026.d", 
          "name": [
            "da/sec \u2013 Biometrics and Internet Security Research Group, Hochschule Darmstadt, Darmstadt, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rathgeb", 
        "givenName": "Christian", 
        "id": "sg:person.012007746513.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012007746513.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "secunet Security Networks AG, Essen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tams", 
        "givenName": "Benjamin", 
        "id": "sg:person.016601437543.93", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016601437543.93"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Darmstadt University of Applied Sciences", 
          "id": "https://www.grid.ac/institutes/grid.449026.d", 
          "name": [
            "da/sec \u2013 Biometrics and Internet Security Research Group, Hochschule Darmstadt, Darmstadt, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wagner", 
        "givenName": "Johannes", 
        "id": "sg:person.010673746743.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010673746743.40"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Darmstadt University of Applied Sciences", 
          "id": "https://www.grid.ac/institutes/grid.449026.d", 
          "name": [
            "da/sec \u2013 Biometrics and Internet Security Research Group, Hochschule Darmstadt, Darmstadt, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Busch", 
        "givenName": "Christoph", 
        "id": "sg:person.011143356603.69", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011143356603.69"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-0-387-71041-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003713634", 
          "https://doi.org/10.1007/978-0-387-71041-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-387-71041-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003713634", 
          "https://doi.org/10.1007/978-0-387-71041-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-04474-8_11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007240803", 
          "https://doi.org/10.1007/978-3-642-04474-8_11"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cviu.2007.08.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011403285"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5772/52152", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013227319"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patrec.2009.07.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019036215"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1020021467", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4614-5571-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020021467", 
          "https://doi.org/10.1007/978-1-4614-5571-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4614-5571-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020021467", 
          "https://doi.org/10.1007/978-1-4614-5571-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/9780470522356.ch26", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022429337"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ins.2012.01.042", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023782675"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4471-6524-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023995742", 
          "https://doi.org/10.1007/978-1-4471-6524-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4471-6524-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023995742", 
          "https://doi.org/10.1007/978-1-4471-6524-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/982507.982516", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024182263"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10623-005-6343-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033876746", 
          "https://doi.org/10.1007/s10623-005-6343-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10623-005-6343-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033876746", 
          "https://doi.org/10.1007/s10623-005-6343-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1687-417x-2011-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045155101", 
          "https://doi.org/10.1186/1687-417x-2011-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cose.2013.12.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045332732"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cviu.2013.06.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048564352"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1587/elex.4.724", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048757286"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0167-8655(03)00079-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053386059"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0167-8655(03)00079-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053386059"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1049/iet-bmt.2011.0001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056818612"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1049/iet-bmt.2014.0093", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056818718"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/18.782097", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061101042"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jproc.2004.827372", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061296270"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mc.2010.44", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061388458"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/msp.2009.932122", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061423266"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/msp.2015.2423693", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061424301"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/msp.2015.2427849", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061424305"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tc.2006.138", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061534228"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcsvt.2003.818349", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061574495"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcsvt.2003.818350", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061574496"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tifs.2007.908165", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061629521"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tifs.2008.2002937", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061629546"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tifs.2010.2046984", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061629722"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tifs.2010.2091637", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061629780"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tifs.2011.2108288", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061629803"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tifs.2011.2166545", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061629918"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tifs.2012.2210215", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061630041"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tifs.2013.2272786", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061630184"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tifs.2015.2392559", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061630470"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2004.827237", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061641029"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2007.1087", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061743212"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2010.227", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061743939"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2011.34", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061744172"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmcb.2008.927261", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061797006"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/060651380", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062849070"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1147/sj.403.0614", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063184645"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1155/2008/657081", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063202759", 
          "https://doi.org/10.1155/2008/657081"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/secpri.1998.674831", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095305013"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-12", 
    "datePublishedReg": "2016-12-01", 
    "description": "Iris recognition technologies are deployed in numerous large-scale nation-wide projects in order to provide robust and reliable biometric recognition of individuals. Moreover, the iris has been found to be rather stable over time, i.e. iris biometric reference data provides a strong and permanent link between individuals and their biometric traits. Hence, unprotected storage of (iris) biometric data provokes serious privacy threats, e.g. identity theft, limited re-newability, or cross-matching. Biometric cryptosystems grant a significant improvement in data privacy and increase the likelihood that individuals will effectively consent in the biometric system usage. However, the vast majority of proposed biometric cryptosystems do not guarantee desired properties of irreversibility, unlinkability, and re-newability without significantly degrading the biometric performance. In this work, we propose an unlinkable multi-instance iris biometric cryptosystem based on the improved fuzzy vault scheme. The proposed system locks biometric feature sets extracted from binary iris biometric reference data, i.e. iris-codes, of the left and right irises in a single fuzzy vault. In order to retain the size of the protected template and authentication speed, the proposed fusion step combines the most discriminative parts of two iris-codes at feature level. It is shown that the proposed key-binding process enables the generation of irreversible protected templates which prevents from previously proposed cross-matching attacks. Further, we investigate the optimal choice among potential decoding strategies with respect to biometric performance and time of key retrieval. The fully reproducible system is integrated to two different publicly available iris recognition systems and evaluated on the CASIAv3-Interval and the IITDv1 iris databases. Compared to the corresponding unprotected recognition schemes, genuine match rates of approximately 95 and 97 % at which no false accepts are observed and maintained in a single- and multi-instance scenario, respectively. Moreover, the multi-iris system is shown to significantly improve privacy protection achieving security levels of approximately 70 bits at practical biometric performance.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s13635-016-0049-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1313801", 
        "issn": [
          "1687-4161", 
          "2510-523X"
        ], 
        "name": "EURASIP Journal on Information Security", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "2016"
      }
    ], 
    "name": "Unlinkable improved multi-biometric iris fuzzy vault", 
    "pagination": "26", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "11e56768903f8639f883034c19a6de1337eb436e3a1156de2431a1846a155a82"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s13635-016-0049-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1003756807"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s13635-016-0049-9", 
      "https://app.dimensions.ai/details/publication/pub.1003756807"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:22", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000362_0000000362/records_87083_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2Fs13635-016-0049-9"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s13635-016-0049-9'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s13635-016-0049-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13635-016-0049-9'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13635-016-0049-9'


 

This table displays all metadata directly associated to this object as RDF triples.

228 TRIPLES      21 PREDICATES      73 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s13635-016-0049-9 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N35ce088d772c4515a76e1ab65f4b4f4e
4 schema:citation sg:pub.10.1007/978-0-387-71041-9
5 sg:pub.10.1007/978-1-4471-6524-8
6 sg:pub.10.1007/978-1-4614-5571-4
7 sg:pub.10.1007/978-3-642-04474-8_11
8 sg:pub.10.1007/s10623-005-6343-z
9 sg:pub.10.1155/2008/657081
10 sg:pub.10.1186/1687-417x-2011-3
11 https://app.dimensions.ai/details/publication/pub.1020021467
12 https://doi.org/10.1002/9780470522356.ch26
13 https://doi.org/10.1016/j.cose.2013.12.005
14 https://doi.org/10.1016/j.cviu.2007.08.005
15 https://doi.org/10.1016/j.cviu.2013.06.003
16 https://doi.org/10.1016/j.ins.2012.01.042
17 https://doi.org/10.1016/j.patrec.2009.07.003
18 https://doi.org/10.1016/s0167-8655(03)00079-5
19 https://doi.org/10.1049/iet-bmt.2011.0001
20 https://doi.org/10.1049/iet-bmt.2014.0093
21 https://doi.org/10.1109/18.782097
22 https://doi.org/10.1109/jproc.2004.827372
23 https://doi.org/10.1109/mc.2010.44
24 https://doi.org/10.1109/msp.2009.932122
25 https://doi.org/10.1109/msp.2015.2423693
26 https://doi.org/10.1109/msp.2015.2427849
27 https://doi.org/10.1109/secpri.1998.674831
28 https://doi.org/10.1109/tc.2006.138
29 https://doi.org/10.1109/tcsvt.2003.818349
30 https://doi.org/10.1109/tcsvt.2003.818350
31 https://doi.org/10.1109/tifs.2007.908165
32 https://doi.org/10.1109/tifs.2008.2002937
33 https://doi.org/10.1109/tifs.2010.2046984
34 https://doi.org/10.1109/tifs.2010.2091637
35 https://doi.org/10.1109/tifs.2011.2108288
36 https://doi.org/10.1109/tifs.2011.2166545
37 https://doi.org/10.1109/tifs.2012.2210215
38 https://doi.org/10.1109/tifs.2013.2272786
39 https://doi.org/10.1109/tifs.2015.2392559
40 https://doi.org/10.1109/tip.2004.827237
41 https://doi.org/10.1109/tpami.2007.1087
42 https://doi.org/10.1109/tpami.2010.227
43 https://doi.org/10.1109/tpami.2011.34
44 https://doi.org/10.1109/tsmcb.2008.927261
45 https://doi.org/10.1137/060651380
46 https://doi.org/10.1145/982507.982516
47 https://doi.org/10.1147/sj.403.0614
48 https://doi.org/10.1587/elex.4.724
49 https://doi.org/10.5772/52152
50 schema:datePublished 2016-12
51 schema:datePublishedReg 2016-12-01
52 schema:description Iris recognition technologies are deployed in numerous large-scale nation-wide projects in order to provide robust and reliable biometric recognition of individuals. Moreover, the iris has been found to be rather stable over time, i.e. iris biometric reference data provides a strong and permanent link between individuals and their biometric traits. Hence, unprotected storage of (iris) biometric data provokes serious privacy threats, e.g. identity theft, limited re-newability, or cross-matching. Biometric cryptosystems grant a significant improvement in data privacy and increase the likelihood that individuals will effectively consent in the biometric system usage. However, the vast majority of proposed biometric cryptosystems do not guarantee desired properties of irreversibility, unlinkability, and re-newability without significantly degrading the biometric performance. In this work, we propose an unlinkable multi-instance iris biometric cryptosystem based on the improved fuzzy vault scheme. The proposed system locks biometric feature sets extracted from binary iris biometric reference data, i.e. iris-codes, of the left and right irises in a single fuzzy vault. In order to retain the size of the protected template and authentication speed, the proposed fusion step combines the most discriminative parts of two iris-codes at feature level. It is shown that the proposed key-binding process enables the generation of irreversible protected templates which prevents from previously proposed cross-matching attacks. Further, we investigate the optimal choice among potential decoding strategies with respect to biometric performance and time of key retrieval. The fully reproducible system is integrated to two different publicly available iris recognition systems and evaluated on the CASIAv3-Interval and the IITDv1 iris databases. Compared to the corresponding unprotected recognition schemes, genuine match rates of approximately 95 and 97 % at which no false accepts are observed and maintained in a single- and multi-instance scenario, respectively. Moreover, the multi-iris system is shown to significantly improve privacy protection achieving security levels of approximately 70 bits at practical biometric performance.
53 schema:genre research_article
54 schema:inLanguage en
55 schema:isAccessibleForFree true
56 schema:isPartOf Nb7d59935ff914f68876c80b8cc0b492d
57 Nd302f783a0574d63aac3d6525c4f10b9
58 sg:journal.1313801
59 schema:name Unlinkable improved multi-biometric iris fuzzy vault
60 schema:pagination 26
61 schema:productId N0fe4e94b974845479540c07a534df406
62 N4d346115d9934bfab9d3121f0da70e30
63 Na704238c73cc4a31b006e6993acc8d98
64 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003756807
65 https://doi.org/10.1186/s13635-016-0049-9
66 schema:sdDatePublished 2019-04-11T12:22
67 schema:sdLicense https://scigraph.springernature.com/explorer/license/
68 schema:sdPublisher Nc3083e156b274bd1a94373b0b9d9f94c
69 schema:url https://link.springer.com/10.1186%2Fs13635-016-0049-9
70 sgo:license sg:explorer/license/
71 sgo:sdDataset articles
72 rdf:type schema:ScholarlyArticle
73 N0154c428371c433cbb045cd2515b2ffe rdf:first sg:person.010673746743.40
74 rdf:rest N27a1fb7ac07d4d208dfd63f6f0a67ced
75 N0fe4e94b974845479540c07a534df406 schema:name dimensions_id
76 schema:value pub.1003756807
77 rdf:type schema:PropertyValue
78 N27a1fb7ac07d4d208dfd63f6f0a67ced rdf:first sg:person.011143356603.69
79 rdf:rest rdf:nil
80 N30e6e6caeb4242bb84bc4bf1d9f88598 rdf:first sg:person.016601437543.93
81 rdf:rest N0154c428371c433cbb045cd2515b2ffe
82 N35ce088d772c4515a76e1ab65f4b4f4e rdf:first sg:person.012007746513.18
83 rdf:rest N30e6e6caeb4242bb84bc4bf1d9f88598
84 N4d346115d9934bfab9d3121f0da70e30 schema:name readcube_id
85 schema:value 11e56768903f8639f883034c19a6de1337eb436e3a1156de2431a1846a155a82
86 rdf:type schema:PropertyValue
87 Na704238c73cc4a31b006e6993acc8d98 schema:name doi
88 schema:value 10.1186/s13635-016-0049-9
89 rdf:type schema:PropertyValue
90 Nb7d59935ff914f68876c80b8cc0b492d schema:volumeNumber 2016
91 rdf:type schema:PublicationVolume
92 Nc3083e156b274bd1a94373b0b9d9f94c schema:name Springer Nature - SN SciGraph project
93 rdf:type schema:Organization
94 Nd302f783a0574d63aac3d6525c4f10b9 schema:issueNumber 1
95 rdf:type schema:PublicationIssue
96 Ndae049bf94684a33a84b0a64280fd1e8 schema:name secunet Security Networks AG, Essen, Germany
97 rdf:type schema:Organization
98 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
99 schema:name Information and Computing Sciences
100 rdf:type schema:DefinedTerm
101 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
102 schema:name Artificial Intelligence and Image Processing
103 rdf:type schema:DefinedTerm
104 sg:journal.1313801 schema:issn 1687-4161
105 2510-523X
106 schema:name EURASIP Journal on Information Security
107 rdf:type schema:Periodical
108 sg:person.010673746743.40 schema:affiliation https://www.grid.ac/institutes/grid.449026.d
109 schema:familyName Wagner
110 schema:givenName Johannes
111 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010673746743.40
112 rdf:type schema:Person
113 sg:person.011143356603.69 schema:affiliation https://www.grid.ac/institutes/grid.449026.d
114 schema:familyName Busch
115 schema:givenName Christoph
116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011143356603.69
117 rdf:type schema:Person
118 sg:person.012007746513.18 schema:affiliation https://www.grid.ac/institutes/grid.449026.d
119 schema:familyName Rathgeb
120 schema:givenName Christian
121 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012007746513.18
122 rdf:type schema:Person
123 sg:person.016601437543.93 schema:affiliation Ndae049bf94684a33a84b0a64280fd1e8
124 schema:familyName Tams
125 schema:givenName Benjamin
126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016601437543.93
127 rdf:type schema:Person
128 sg:pub.10.1007/978-0-387-71041-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003713634
129 https://doi.org/10.1007/978-0-387-71041-9
130 rdf:type schema:CreativeWork
131 sg:pub.10.1007/978-1-4471-6524-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023995742
132 https://doi.org/10.1007/978-1-4471-6524-8
133 rdf:type schema:CreativeWork
134 sg:pub.10.1007/978-1-4614-5571-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020021467
135 https://doi.org/10.1007/978-1-4614-5571-4
136 rdf:type schema:CreativeWork
137 sg:pub.10.1007/978-3-642-04474-8_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007240803
138 https://doi.org/10.1007/978-3-642-04474-8_11
139 rdf:type schema:CreativeWork
140 sg:pub.10.1007/s10623-005-6343-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1033876746
141 https://doi.org/10.1007/s10623-005-6343-z
142 rdf:type schema:CreativeWork
143 sg:pub.10.1155/2008/657081 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063202759
144 https://doi.org/10.1155/2008/657081
145 rdf:type schema:CreativeWork
146 sg:pub.10.1186/1687-417x-2011-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045155101
147 https://doi.org/10.1186/1687-417x-2011-3
148 rdf:type schema:CreativeWork
149 https://app.dimensions.ai/details/publication/pub.1020021467 schema:CreativeWork
150 https://doi.org/10.1002/9780470522356.ch26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022429337
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1016/j.cose.2013.12.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045332732
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1016/j.cviu.2007.08.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011403285
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1016/j.cviu.2013.06.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048564352
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1016/j.ins.2012.01.042 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023782675
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1016/j.patrec.2009.07.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019036215
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1016/s0167-8655(03)00079-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053386059
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1049/iet-bmt.2011.0001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056818612
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1049/iet-bmt.2014.0093 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056818718
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1109/18.782097 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061101042
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1109/jproc.2004.827372 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061296270
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1109/mc.2010.44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061388458
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1109/msp.2009.932122 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061423266
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1109/msp.2015.2423693 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061424301
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1109/msp.2015.2427849 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061424305
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1109/secpri.1998.674831 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095305013
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1109/tc.2006.138 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061534228
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1109/tcsvt.2003.818349 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061574495
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1109/tcsvt.2003.818350 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061574496
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1109/tifs.2007.908165 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061629521
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1109/tifs.2008.2002937 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061629546
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1109/tifs.2010.2046984 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061629722
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1109/tifs.2010.2091637 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061629780
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1109/tifs.2011.2108288 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061629803
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1109/tifs.2011.2166545 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061629918
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1109/tifs.2012.2210215 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061630041
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1109/tifs.2013.2272786 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061630184
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1109/tifs.2015.2392559 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061630470
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1109/tip.2004.827237 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061641029
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1109/tpami.2007.1087 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061743212
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1109/tpami.2010.227 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061743939
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1109/tpami.2011.34 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061744172
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1109/tsmcb.2008.927261 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061797006
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1137/060651380 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062849070
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1145/982507.982516 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024182263
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1147/sj.403.0614 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063184645
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1587/elex.4.724 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048757286
223 rdf:type schema:CreativeWork
224 https://doi.org/10.5772/52152 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013227319
225 rdf:type schema:CreativeWork
226 https://www.grid.ac/institutes/grid.449026.d schema:alternateName Darmstadt University of Applied Sciences
227 schema:name da/sec – Biometrics and Internet Security Research Group, Hochschule Darmstadt, Darmstadt, Germany
228 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...