{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/34010"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/34010","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Minutiae Triplet-based Features with Extended Ridge Information for Determining Sufficiency in Fingerprints","abstract":"In order to deliver statistical and qualitative backing to latent fingerprint evidence, algorithms are proposed (1) to perform fingerprint matching to aid in quality assessment, and (2) to discover statistically rare features or patterns in fingerprints. These features would help establish an objective minimum-quality baseline for latent prints as well as aid in the latent examination process in making a matching comparison. The proposed methodologies use minutiae triplet-based features in a hierarchical fashion, where not only minutia points are used, but ridge information is used to help establish relations between minutiae. Results show (1) that our triplet-based descriptor is useful in eliminating false matches in the matching algorithm, and (2) that a set of distinctive features can be found that have sufficient discriminatory power to aid in quality assessment.","abstract_html":"In order to deliver statistical and qualitative backing to latent fingerprint evidence, algorithms are proposed (1) to perform fingerprint matching to aid in quality assessment, and (2) to discover statistically rare features or patterns in fingerprints. These features would help establish an objective minimum-quality baseline for latent prints as well as aid in the latent examination process in making a matching comparison. The proposed methodologies use minutiae triplet-based features in a hierarchical fashion, where not only minutia points are used, but ridge information is used to help establish relations between minutiae. Results show (1) that our triplet-based descriptor is useful in eliminating false matches in the matching algorithm, and (2) that a set of distinctive features can be found that have sufficient discriminatory power to aid in quality assessment.","abstract_has_math":false,"creators":["Hoyle, Kevin"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical and Computer Engineering","degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Hsiao, Michael S."],"committee_members":["Fox, Edward A.","Abbott, A. Lynn"],"year":2011,"date_issued":"2011-07-06","date_published":"2011-07-06","updated_at":"2026-07-22T22:20:11Z","subjects":["minutia","sufficiency","latent","quality","fingerprints","friction ridges","triangles","triplets"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-07142011-112818"],"render_values":[{"text":"etd-07142011-112818","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/34010","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":["Fox, Edward A.","Abbott, A. Lynn"]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Hoyle, Kevin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:41:27Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:41:27Z","2011-07-21"]},{"key":"dc:date.issued","label":"Date","values":["2011-07-06"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["minutia","sufficiency","latent","quality","fingerprints","friction ridges","triangles","triplets"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-07142011-112818"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/34010"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In order to deliver statistical and qualitative backing to latent fingerprint evidence, algorithms are proposed (1) to perform fingerprint matching to aid in quality assessment, and (2) to discover statistically rare features or patterns in fingerprints. These features would help establish an objective minimum-quality baseline for latent prints as well as aid in the latent examination process in making a matching comparison. The proposed methodologies use minutiae triplet-based features in a hierarchical fashion, where not only minutia points are used, but ridge information is used to help establish relations between minutiae. Results show (1) that our triplet-based descriptor is useful in eliminating false matches in the matching algorithm, and (2) that a set of distinctive features can be found that have sufficient discriminatory power to aid in quality assessment."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Minutiae Triplet-based Features with Extended Ridge Information for Determining Sufficiency in Fingerprints"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Hsiao, Michael S."],"dc:contributor.committeemember":["Fox, Edward A.","Abbott, A. Lynn"],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Hoyle, Kevin"],"dc:date.accessioned":["2014-03-14T20:41:27Z"],"dc:date.available":["2014-03-14T20:41:27Z","2011-07-21"],"dc:date.issued":["2011-07-06"],"dc:description.abstract":["In order to deliver statistical and qualitative backing to latent fingerprint evidence, algorithms are proposed (1) to perform fingerprint matching to aid in quality assessment, and (2) to discover statistically rare features or patterns in fingerprints. These features would help establish an objective minimum-quality baseline for latent prints as well as aid in the latent examination process in making a matching comparison. The proposed methodologies use minutiae triplet-based features in a hierarchical fashion, where not only minutia points are used, but ridge information is used to help establish relations between minutiae. Results show (1) that our triplet-based descriptor is useful in eliminating false matches in the matching algorithm, and (2) that a set of distinctive features can be found that have sufficient discriminatory power to aid in quality assessment."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-07142011-112818"],"dc:identifier.uri":["http://hdl.handle.net/10919/34010"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["minutia","sufficiency","latent","quality","fingerprints","friction ridges","triangles","triplets"],"dc:title":["Minutiae Triplet-based Features with Extended Ridge Information for Determining Sufficiency in Fingerprints"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:11Z"}