Evidential Value of Automated Latent Fingerprint Comparison: An Empirical Approach
Latent prints are routinely recovered from crime scenes and are compared with available databases of known fingerprints for identifying criminals. However, current procedures to compare latent prints to large databases of exemplar (rolled or plain) prints are prone to errors. This suggests caution i...
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Veröffentlicht in: | IEEE transactions on information forensics and security 2012-12, Vol.7 (6), p.1752-1765 |
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description | Latent prints are routinely recovered from crime scenes and are compared with available databases of known fingerprints for identifying criminals. However, current procedures to compare latent prints to large databases of exemplar (rolled or plain) prints are prone to errors. This suggests caution in making conclusions about a suspect's identity based on a latent fingerprint comparison. A number of attempts have been made to statistically model the utility of a fingerprint comparison in making a correct accept/reject decision or its evidential value. These approaches, however, either make unrealistic assumptions about the model or they lack simple interpretation. We argue that the posterior probability of two fingerprints belonging to different fingers given their match score, referred to as the nonmatch probability (NMP), effectively captures any implicating evidence of the comparison. NMP is computed using state-of-the-art matchers and is easy to interpret. To incorporate the effect of image quality, number of minutiae, and size of the latent on NMP value, we compute the NMP vs. match score plots separately for image pairs (latent and exemplar prints) with different characteristics. Given the paucity of latent fingerprint databases in public domain, we simulate latent prints using two exemplar print databases (NIST SD-14 and Michigan State Police) by cropping regions of three different sizes. We appropriately validate this simulation using four latent databases (NIST SD-27 and three proprietary latent databases) and two state-of-the-art fingerprint matchers to compute their respective match scores. We also discuss a practical scenario where a latent examiner uses the proposed framework to compute the evidential value of a latent-exemplar print pair comparison. |
doi_str_mv | 10.1109/TIFS.2012.2210216 |
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K.</creator><creatorcontrib>Nagar, A. ; Heeseung Choi ; Jain, A. K.</creatorcontrib><description>Latent prints are routinely recovered from crime scenes and are compared with available databases of known fingerprints for identifying criminals. However, current procedures to compare latent prints to large databases of exemplar (rolled or plain) prints are prone to errors. This suggests caution in making conclusions about a suspect's identity based on a latent fingerprint comparison. A number of attempts have been made to statistically model the utility of a fingerprint comparison in making a correct accept/reject decision or its evidential value. These approaches, however, either make unrealistic assumptions about the model or they lack simple interpretation. We argue that the posterior probability of two fingerprints belonging to different fingers given their match score, referred to as the nonmatch probability (NMP), effectively captures any implicating evidence of the comparison. NMP is computed using state-of-the-art matchers and is easy to interpret. To incorporate the effect of image quality, number of minutiae, and size of the latent on NMP value, we compute the NMP vs. match score plots separately for image pairs (latent and exemplar prints) with different characteristics. Given the paucity of latent fingerprint databases in public domain, we simulate latent prints using two exemplar print databases (NIST SD-14 and Michigan State Police) by cropping regions of three different sizes. We appropriately validate this simulation using four latent databases (NIST SD-27 and three proprietary latent databases) and two state-of-the-art fingerprint matchers to compute their respective match scores. We also discuss a practical scenario where a latent examiner uses the proposed framework to compute the evidential value of a latent-exemplar print pair comparison.</description><identifier>ISSN: 1556-6013</identifier><identifier>EISSN: 1556-6021</identifier><identifier>DOI: 10.1109/TIFS.2012.2210216</identifier><identifier>CODEN: ITIFA6</identifier><language>eng</language><publisher>IEEE</publisher><subject>Error analysis ; evidential value ; Fingerprint matching ; Fingerprint recognition ; Forensics ; genuine match distribution ; impostor match distribution ; individuality ; latent print comparison ; non-match probability ; Pattern recognition ; Protocols</subject><ispartof>IEEE transactions on information forensics and security, 2012-12, Vol.7 (6), p.1752-1765</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c308t-f1311a014ba8d4c909d40ccdca395211eeaa6a9b3c3ebf7476d826187ac42cbd3</citedby><cites>FETCH-LOGICAL-c308t-f1311a014ba8d4c909d40ccdca395211eeaa6a9b3c3ebf7476d826187ac42cbd3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6248213$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6248213$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Nagar, A.</creatorcontrib><creatorcontrib>Heeseung Choi</creatorcontrib><creatorcontrib>Jain, A. K.</creatorcontrib><title>Evidential Value of Automated Latent Fingerprint Comparison: An Empirical Approach</title><title>IEEE transactions on information forensics and security</title><addtitle>TIFS</addtitle><description>Latent prints are routinely recovered from crime scenes and are compared with available databases of known fingerprints for identifying criminals. However, current procedures to compare latent prints to large databases of exemplar (rolled or plain) prints are prone to errors. This suggests caution in making conclusions about a suspect's identity based on a latent fingerprint comparison. A number of attempts have been made to statistically model the utility of a fingerprint comparison in making a correct accept/reject decision or its evidential value. These approaches, however, either make unrealistic assumptions about the model or they lack simple interpretation. We argue that the posterior probability of two fingerprints belonging to different fingers given their match score, referred to as the nonmatch probability (NMP), effectively captures any implicating evidence of the comparison. NMP is computed using state-of-the-art matchers and is easy to interpret. To incorporate the effect of image quality, number of minutiae, and size of the latent on NMP value, we compute the NMP vs. match score plots separately for image pairs (latent and exemplar prints) with different characteristics. Given the paucity of latent fingerprint databases in public domain, we simulate latent prints using two exemplar print databases (NIST SD-14 and Michigan State Police) by cropping regions of three different sizes. We appropriately validate this simulation using four latent databases (NIST SD-27 and three proprietary latent databases) and two state-of-the-art fingerprint matchers to compute their respective match scores. We also discuss a practical scenario where a latent examiner uses the proposed framework to compute the evidential value of a latent-exemplar print pair comparison.</description><subject>Error analysis</subject><subject>evidential value</subject><subject>Fingerprint matching</subject><subject>Fingerprint recognition</subject><subject>Forensics</subject><subject>genuine match distribution</subject><subject>impostor match distribution</subject><subject>individuality</subject><subject>latent print comparison</subject><subject>non-match probability</subject><subject>Pattern recognition</subject><subject>Protocols</subject><issn>1556-6013</issn><issn>1556-6021</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kN1KxDAQRoMouK4-gHiTF2jNJGnaelfKri4UBF29LdMk1Uj_SLuCb2_LLnsz8w3MGYZDyD2wEIClj_vd9j3kDHjIOTAO6oKsIIpUoObh8pxBXJObcfxhTEpQyYq8bX6dsd3ksKGf2Bws7WuaHaa-xckaWsy1m-jWdV_WD97NOe_bAb0b--6JZh3dtIPzTs94Ngy-R_19S65qbEZ7d-pr8rHd7POXoHh93uVZEWjBkimoQQAgA1lhYqROWWok09poFGnEAaxFVJhWQgtb1bGMlUm4giRGLbmujFgTON7Vvh9Hb-tyfrBF_1cCKxcp5SKlXKSUJykz83BknLX2vK-4TDgI8Q_qeF6s</recordid><startdate>20121201</startdate><enddate>20121201</enddate><creator>Nagar, A.</creator><creator>Heeseung Choi</creator><creator>Jain, A. K.</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20121201</creationdate><title>Evidential Value of Automated Latent Fingerprint Comparison: An Empirical Approach</title><author>Nagar, A. ; Heeseung Choi ; Jain, A. K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c308t-f1311a014ba8d4c909d40ccdca395211eeaa6a9b3c3ebf7476d826187ac42cbd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Error analysis</topic><topic>evidential value</topic><topic>Fingerprint matching</topic><topic>Fingerprint recognition</topic><topic>Forensics</topic><topic>genuine match distribution</topic><topic>impostor match distribution</topic><topic>individuality</topic><topic>latent print comparison</topic><topic>non-match probability</topic><topic>Pattern recognition</topic><topic>Protocols</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nagar, A.</creatorcontrib><creatorcontrib>Heeseung Choi</creatorcontrib><creatorcontrib>Jain, A. K.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><jtitle>IEEE transactions on information forensics and security</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Nagar, A.</au><au>Heeseung Choi</au><au>Jain, A. K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evidential Value of Automated Latent Fingerprint Comparison: An Empirical Approach</atitle><jtitle>IEEE transactions on information forensics and security</jtitle><stitle>TIFS</stitle><date>2012-12-01</date><risdate>2012</risdate><volume>7</volume><issue>6</issue><spage>1752</spage><epage>1765</epage><pages>1752-1765</pages><issn>1556-6013</issn><eissn>1556-6021</eissn><coden>ITIFA6</coden><abstract>Latent prints are routinely recovered from crime scenes and are compared with available databases of known fingerprints for identifying criminals. However, current procedures to compare latent prints to large databases of exemplar (rolled or plain) prints are prone to errors. This suggests caution in making conclusions about a suspect's identity based on a latent fingerprint comparison. A number of attempts have been made to statistically model the utility of a fingerprint comparison in making a correct accept/reject decision or its evidential value. These approaches, however, either make unrealistic assumptions about the model or they lack simple interpretation. We argue that the posterior probability of two fingerprints belonging to different fingers given their match score, referred to as the nonmatch probability (NMP), effectively captures any implicating evidence of the comparison. NMP is computed using state-of-the-art matchers and is easy to interpret. To incorporate the effect of image quality, number of minutiae, and size of the latent on NMP value, we compute the NMP vs. match score plots separately for image pairs (latent and exemplar prints) with different characteristics. Given the paucity of latent fingerprint databases in public domain, we simulate latent prints using two exemplar print databases (NIST SD-14 and Michigan State Police) by cropping regions of three different sizes. We appropriately validate this simulation using four latent databases (NIST SD-27 and three proprietary latent databases) and two state-of-the-art fingerprint matchers to compute their respective match scores. We also discuss a practical scenario where a latent examiner uses the proposed framework to compute the evidential value of a latent-exemplar print pair comparison.</abstract><pub>IEEE</pub><doi>10.1109/TIFS.2012.2210216</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Error analysis evidential value Fingerprint matching Fingerprint recognition Forensics genuine match distribution impostor match distribution individuality latent print comparison non-match probability Pattern recognition Protocols |
title | Evidential Value of Automated Latent Fingerprint Comparison: An Empirical Approach |
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