{"id":"https://openalex.org/W1994910053","doi":"https://doi.org/10.1109/btas.2014.6996298","title":"Learning to predict match scores for iris image quality assessment","display_name":"Learning to predict match scores for iris image quality assessment","publication_year":2014,"publication_date":"2014-09-01","ids":{"openalex":"https://openalex.org/W1994910053","doi":"https://doi.org/10.1109/btas.2014.6996298","mag":"1994910053"},"language":"en","primary_location":{"id":"doi:10.1109/btas.2014.6996298","is_oa":false,"landing_page_url":"https://doi.org/10.1109/btas.2014.6996298","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE International Joint Conference on Biometrics","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003315670","display_name":"Michael Happold","orcid":null},"institutions":[{"id":"https://openalex.org/I4210102071","display_name":"Neya Systems (United States)","ror":"https://ror.org/0171fce94","country_code":"US","type":"company","lineage":["https://openalex.org/I4210102071"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Michael Happold","raw_affiliation_strings":["Neya Systems, LLC, Wexford, PA","Neya Systems, LLC, 145 Lake Dr, Wexford, PA 16033, USA"],"affiliations":[{"raw_affiliation_string":"Neya Systems, LLC, Wexford, PA","institution_ids":["https://openalex.org/I4210102071"]},{"raw_affiliation_string":"Neya Systems, LLC, 145 Lake Dr, Wexford, PA 16033, USA","institution_ids":["https://openalex.org/I4210102071"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5003315670"],"corresponding_institution_ids":["https://openalex.org/I4210102071"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.07152364,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"2008","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9221000075340271,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7040492296218872},{"id":"https://openalex.org/keywords/iris-recognition","display_name":"Iris recognition","score":0.6213672161102295},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6134557127952576},{"id":"https://openalex.org/keywords/iris","display_name":"IRIS (biosensor)","score":0.567865252494812},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5085307359695435},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.48986098170280457},{"id":"https://openalex.org/keywords/quality-assessment","display_name":"Quality assessment","score":0.458151251077652},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39785686135292053},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3961562514305115},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.33521974086761475},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.14951792359352112},{"id":"https://openalex.org/keywords/evaluation-methods","display_name":"Evaluation methods","score":0.12188047170639038},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.11344590783119202},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09477967023849487}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7040492296218872},{"id":"https://openalex.org/C112356035","wikidata":"https://www.wikidata.org/wiki/Q1672722","display_name":"Iris recognition","level":3,"score":0.6213672161102295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6134557127952576},{"id":"https://openalex.org/C2779503344","wikidata":"https://www.wikidata.org/wiki/Q5973514","display_name":"IRIS (biosensor)","level":3,"score":0.567865252494812},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5085307359695435},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.48986098170280457},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.458151251077652},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39785686135292053},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3961562514305115},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.33521974086761475},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.14951792359352112},{"id":"https://openalex.org/C3018395757","wikidata":"https://www.wikidata.org/wiki/Q1379672","display_name":"Evaluation methods","level":2,"score":0.12188047170639038},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.11344590783119202},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09477967023849487},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/btas.2014.6996298","is_oa":false,"landing_page_url":"https://doi.org/10.1109/btas.2014.6996298","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE International Joint Conference on Biometrics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306110","display_name":"U.S. Department of Homeland Security","ror":"https://ror.org/00jyr0d86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W190437827","https://openalex.org/W1495061682","https://openalex.org/W1549412372","https://openalex.org/W1557986494","https://openalex.org/W1585381382","https://openalex.org/W1599871777","https://openalex.org/W1808644423","https://openalex.org/W1974821667","https://openalex.org/W1997226303","https://openalex.org/W2008338741","https://openalex.org/W2017337590","https://openalex.org/W2031342017","https://openalex.org/W2096121634","https://openalex.org/W2108856176","https://openalex.org/W2111992298","https://openalex.org/W2126680738","https://openalex.org/W2128302979","https://openalex.org/W2133295669","https://openalex.org/W2140266075","https://openalex.org/W2149221572","https://openalex.org/W2161969291","https://openalex.org/W2911964244","https://openalex.org/W4212883601","https://openalex.org/W4285719527","https://openalex.org/W6607693206","https://openalex.org/W6633377651","https://openalex.org/W6634937374","https://openalex.org/W6635930888","https://openalex.org/W6681053624"],"related_works":["https://openalex.org/W2086178285","https://openalex.org/W2002861633","https://openalex.org/W1581329577","https://openalex.org/W2113465366","https://openalex.org/W2004816544","https://openalex.org/W2370714421","https://openalex.org/W2295388986","https://openalex.org/W2506815316","https://openalex.org/W2098168024","https://openalex.org/W2388004624"],"abstract_inverted_index":{"Individual":[0],"image":[1,11,34,62,75,84,135,145],"quality":[2,45,63,136,155,161],"metrics":[3],"that":[4,131],"focus":[5],"on":[6,138],"a":[7,40,58,66,92,154],"particular":[8],"form":[9],"of":[10,16,24,46,52,61,105,119,123,134,159],"degradation":[12,137],"have":[13],"the":[14,22,29,33,44,50,77,86,103,113,132],"virtue":[15],"being":[17],"readily":[18],"decipherable":[19],"but":[20],"also":[21],"drawback":[23],"not":[25],"relating":[26],"directly":[27],"to":[28,96,110],"purpose":[30],"for":[31,42,80],"which":[32],"is":[35,126],"used.":[36],"We":[37,56,89],"describe":[38],"here":[39],"method":[41,129],"learning":[43],"iris":[47,53],"images":[48,162],"from":[49,144],"output":[51],"matching":[54],"algorithms.":[55],"extract":[57],"large":[59],"number":[60],"features":[64],"forming":[65],"high":[67],"dimensional":[68],"feature":[69,108,114,124],"vector":[70,115],"and":[71,101],"label":[72],"each":[73],"training":[74,106],"with":[76],"match":[78,99,139,150],"score":[79,151],"its":[81],"corresponding":[82],"genuine":[83,149],"in":[85,102],"enrolled":[87],"database.":[88],"then":[90],"train":[91],"Random":[93],"Forest":[94],"regressor":[95],"predict":[97],"this":[98],"score,":[100],"course":[104],"apply":[107],"selection":[109,125],"dramatically":[111],"reduce":[112],"dimensionality.":[116],"A":[117],"comparison":[118],"several":[120],"alternative":[121],"methods":[122],"given.":[127],"Our":[128],"demonstrates":[130],"effects":[133],"scores":[140],"can":[141],"be":[142],"predicted":[143,148],"features.":[146],"The":[147],"serves":[152],"as":[153],"metric,":[156],"enabling":[157],"filtering":[158],"poor":[160],"before":[163],"enrollment":[164],"or":[165],"identification.":[166]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
