{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/24784"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/24784","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Mining Rare Features in Fingerprints using Core points and Triplet-based Features","abstract":"A fingerprint matching algorithm with a novel set of matching parameters based on core points and triangular descriptors is proposed to discover rarity in fingerprints. The algorithm uses a mathematical and statistical approach to discover rare features in fingerprints which provides scientific validation for both ten-print and latent fingerprint evidence. A feature is considered rare if it is statistically uncommon; that is, the rare feature should be unique among N (N>100) randomly sampled prints. A rare feature in a fingerprint has higher discriminatory power when it is identified in a print (latent or otherwise). In the case of latent fingerprint matching, the enhanced discriminatory power from the rare features can help in delivering a confident court judgment. In addition to mining the rare features, a parallel algorithm for fingerprint matching on GPUs is also proposed to reduce the run-time of fingerprint matching on larger databases. Results show that 1) matching algorithm is useful in eliminating false matches. 2) each of the 30 fingerprints randomly selected to mine rare features have a small set of highly distinctive statistically rare features some of whose occurrence is one in 1000 fingerprints. 3) the parallel algorithm implemented on GPUs for larger databases is around 40 times faster than the sequential algorithm.","abstract_html":"A fingerprint matching algorithm with a novel set of matching parameters based on core points and triangular descriptors is proposed to discover rarity in fingerprints. The algorithm uses a mathematical and statistical approach to discover rare features in fingerprints which provides scientific validation for both ten-print and latent fingerprint evidence. A feature is considered rare if it is statistically uncommon; that is, the rare feature should be unique among N (N>100) randomly sampled prints. A rare feature in a fingerprint has higher discriminatory power when it is identified in a print (latent or otherwise). In the case of latent fingerprint matching, the enhanced discriminatory power from the rare features can help in delivering a confident court judgment. In addition to mining the rare features, a parallel algorithm for fingerprint matching on GPUs is also proposed to reduce the run-time of fingerprint matching on larger databases. Results show that 1) matching algorithm is useful in eliminating false matches. 2) each of the 30 fingerprints randomly selected to mine rare features have a small set of highly distinctive statistically rare features some of whose occurrence is one in 1000 fingerprints. 3) the parallel algorithm implemented on GPUs for larger databases is around 40 times faster than the sequential algorithm.","abstract_has_math":false,"creators":["Munagani, Indira Priya Darshini"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Engineering","degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Hsiao, Michael S."],"committee_members":["Abbott, A. Lynn","Shukla, Sandeep K."],"year":2014,"date_issued":"2014-01-04","date_published":"2014-01-04","updated_at":"2026-07-22T22:20:40Z","subjects":["Fingerprints","Rare Features","Rarity","Latent","Core Points","Triplets","GPU"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:2156"],"render_values":[{"text":"vt_gsexam:2156","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/24784","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Hsiao, Michael S."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Abbott, A. 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The algorithm uses a mathematical and statistical approach to discover rare features in fingerprints which provides scientific validation for both ten-print and latent fingerprint evidence. A feature is considered rare if it is statistically uncommon; that is, the rare feature should be unique among N (N>100) randomly sampled prints. A rare feature in a fingerprint has higher discriminatory power when it is identified in a print (latent or otherwise). In the case of latent fingerprint matching, the enhanced discriminatory power from the rare features can help in delivering a confident court judgment. In addition to mining the rare features, a parallel algorithm for fingerprint matching on GPUs is also proposed to reduce the run-time of fingerprint matching on larger databases. Results show that 1) matching algorithm is useful in eliminating false matches. 2) each of the 30 fingerprints randomly selected to mine rare features have a small set of highly distinctive statistically rare features some of whose occurrence is one in 1000 fingerprints. 3) the parallel algorithm implemented on GPUs for larger databases is around 40 times faster than the sequential algorithm."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Mining Rare Features in Fingerprints using Core points and Triplet-based Features"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Hsiao, Michael S."],"dc:contributor.committeemember":["Abbott, A. Lynn","Shukla, Sandeep K."],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Munagani, Indira Priya Darshini"],"dc:date.accessioned":["2014-01-05T09:00:06Z"],"dc:date.available":["2014-01-05T09:00:06Z"],"dc:date.issued":["2014-01-04"],"dc:description.abstract":["A fingerprint matching algorithm with a novel set of matching parameters based on core points and triangular descriptors is proposed to discover rarity in fingerprints. The algorithm uses a mathematical and statistical approach to discover rare features in fingerprints which provides scientific validation for both ten-print and latent fingerprint evidence. A feature is considered rare if it is statistically uncommon; that is, the rare feature should be unique among N (N>100) randomly sampled prints. A rare feature in a fingerprint has higher discriminatory power when it is identified in a print (latent or otherwise). In the case of latent fingerprint matching, the enhanced discriminatory power from the rare features can help in delivering a confident court judgment. In addition to mining the rare features, a parallel algorithm for fingerprint matching on GPUs is also proposed to reduce the run-time of fingerprint matching on larger databases. Results show that 1) matching algorithm is useful in eliminating false matches. 2) each of the 30 fingerprints randomly selected to mine rare features have a small set of highly distinctive statistically rare features some of whose occurrence is one in 1000 fingerprints. 3) the parallel algorithm implemented on GPUs for larger databases is around 40 times faster than the sequential algorithm."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:2156"],"dc:identifier.uri":["http://hdl.handle.net/10919/24784"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Fingerprints","Rare Features","Rarity","Latent","Core Points","Triplets","GPU"],"dc:title":["Mining Rare Features in Fingerprints using Core points and Triplet-based Features"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:40Z"}