Can point-cloud based neural networks learn fingerprint variability?
Subject- and environmental-specific variations affect the fingerprint recognition process. Quality metrics are capable of detecting and rating severe degradations. However, measuring natural variability, occurring during different fingerprint acquisitions, is not in the scope of these metrics. This...
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| creator | Sollinger, Dominik Jochl, Robert Kirchgasser, Simon Uhl, Andreas |
| description | Subject- and environmental-specific variations affect the fingerprint recognition process. Quality metrics are capable of detecting and rating severe degradations. However, measuring natural variability, occurring during different fingerprint acquisitions, is not in the scope of these metrics. This work proposes the use of genuine comparison scores as a measure of variability. It is shown that the publicly available PLUS-MSL-FP dataset exhibits large natural variations which can be used to distinguish between different acquisition sessions. Furthermore, it is showcased that point-cloud (set) based neural networks are promising candidates for processing fingerprint imagery as they provide precise control over the input parameters. Experiments show that point-cloud based neural networks are capable of distinguishing between the different sessions in the PLUS-MSL-FP dataset solely based on FP minutiae locations. |
| doi_str_mv | 10.1109/BIOSIG55365.2022.9897050 |
| format | Conference Proceeding |
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Quality metrics are capable of detecting and rating severe degradations. However, measuring natural variability, occurring during different fingerprint acquisitions, is not in the scope of these metrics. This work proposes the use of genuine comparison scores as a measure of variability. It is shown that the publicly available PLUS-MSL-FP dataset exhibits large natural variations which can be used to distinguish between different acquisition sessions. Furthermore, it is showcased that point-cloud (set) based neural networks are promising candidates for processing fingerprint imagery as they provide precise control over the input parameters. 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Quality metrics are capable of detecting and rating severe degradations. However, measuring natural variability, occurring during different fingerprint acquisitions, is not in the scope of these metrics. This work proposes the use of genuine comparison scores as a measure of variability. It is shown that the publicly available PLUS-MSL-FP dataset exhibits large natural variations which can be used to distinguish between different acquisition sessions. Furthermore, it is showcased that point-cloud (set) based neural networks are promising candidates for processing fingerprint imagery as they provide precise control over the input parameters. Experiments show that point-cloud based neural networks are capable of distinguishing between the different sessions in the PLUS-MSL-FP dataset solely based on FP minutiae locations.</description><subject>Aging</subject><subject>deep learning</subject><subject>Degradation</subject><subject>fingerprint ageing</subject><subject>Fingerprint recognition</subject><subject>fingerprint similarity</subject><subject>fingerprint variability</subject><subject>Image matching</subject><subject>Measurement</subject><subject>Neural networks</subject><subject>point-cloud</subject><subject>Process control</subject><issn>1617-5468</issn><isbn>1665476664</isbn><isbn>9781665476669</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2022</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj89KxDAYxKMguK77BF7yAq35-yU5iVZdCwt7UM9L2nyVaG2XtKvs2xtwh4HfZRhmCKGclZwzd_tQb1_rtdYSdCmYEKWzzjDNzsgVB9DKAIA6JwsO3BRagb0kq2n6ZFmGCW3sgjxWfqD7MQ5z0fbjIdDGTxjogIfk-4z5d0xfE-3Rp4F2cfjAtE85TX98ir6JfZyPd9fkovP9hKsTl-T9-emteik223Vd3W-KyKWcC4mcQecaKYNvhNRNBy4Ey703qLRqrQsCWmytcAaRa4nahI4rJRVkK7kkN_-9ERF3ece3T8fd6bT8A1DnTP4</recordid><startdate>202209</startdate><enddate>202209</enddate><creator>Sollinger, Dominik</creator><creator>Jochl, Robert</creator><creator>Kirchgasser, Simon</creator><creator>Uhl, Andreas</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>202209</creationdate><title>Can point-cloud based neural networks learn fingerprint variability?</title><author>Sollinger, Dominik ; Jochl, Robert ; Kirchgasser, Simon ; Uhl, Andreas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i133t-3e106f9b33dab235bf69dd81aa7e454c89d26cec8297ee153e57df14434634643</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aging</topic><topic>deep learning</topic><topic>Degradation</topic><topic>fingerprint ageing</topic><topic>Fingerprint recognition</topic><topic>fingerprint similarity</topic><topic>fingerprint variability</topic><topic>Image matching</topic><topic>Measurement</topic><topic>Neural networks</topic><topic>point-cloud</topic><topic>Process control</topic><toplevel>online_resources</toplevel><creatorcontrib>Sollinger, Dominik</creatorcontrib><creatorcontrib>Jochl, Robert</creatorcontrib><creatorcontrib>Kirchgasser, Simon</creatorcontrib><creatorcontrib>Uhl, Andreas</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sollinger, Dominik</au><au>Jochl, Robert</au><au>Kirchgasser, Simon</au><au>Uhl, Andreas</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Can point-cloud based neural networks learn fingerprint variability?</atitle><btitle>2022 International Conference of the Biometrics Special Interest Group (BIOSIG)</btitle><stitle>BIOSIG55365</stitle><date>2022-09</date><risdate>2022</risdate><spage>1</spage><epage>8</epage><pages>1-8</pages><eissn>1617-5468</eissn><eisbn>1665476664</eisbn><eisbn>9781665476669</eisbn><abstract>Subject- and environmental-specific variations affect the fingerprint recognition process. Quality metrics are capable of detecting and rating severe degradations. However, measuring natural variability, occurring during different fingerprint acquisitions, is not in the scope of these metrics. This work proposes the use of genuine comparison scores as a measure of variability. It is shown that the publicly available PLUS-MSL-FP dataset exhibits large natural variations which can be used to distinguish between different acquisition sessions. Furthermore, it is showcased that point-cloud (set) based neural networks are promising candidates for processing fingerprint imagery as they provide precise control over the input parameters. Experiments show that point-cloud based neural networks are capable of distinguishing between the different sessions in the PLUS-MSL-FP dataset solely based on FP minutiae locations.</abstract><pub>IEEE</pub><doi>10.1109/BIOSIG55365.2022.9897050</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 1617-5468 |
| ispartof | 2022 International Conference of the Biometrics Special Interest Group (BIOSIG), 2022, p.1-8 |
| issn | 1617-5468 |
| language | eng |
| recordid | cdi_ieee_primary_9897050 |
| source | EZB-FREE-00999 freely available EZB journals |
| subjects | Aging deep learning Degradation fingerprint ageing Fingerprint recognition fingerprint similarity fingerprint variability Image matching Measurement Neural networks point-cloud Process control |
| title | Can point-cloud based neural networks learn fingerprint variability? |
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