{"id":"https://openalex.org/W7161702153","doi":"https://doi.org/10.48550/arxiv.2605.17489","title":"Employing Vision-Language Models for Face Image Quality Assessment","display_name":"Employing Vision-Language Models for Face Image Quality Assessment","publication_year":2026,"publication_date":"2026-05-17","ids":{"openalex":"https://openalex.org/W7161702153","doi":"https://doi.org/10.48550/arxiv.2605.17489"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.17489","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17489","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.17489","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109729568","display_name":"Erdi Sar\u0131ta\u015f","orcid":"https://orcid.org/0009-0001-0493-6792"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sar\u0131ta\u015f, Erdi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115505743","display_name":"Eren Onaran","orcid":"https://orcid.org/0009-0003-7369-4023"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Onaran, Eren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136482107","display_name":"Vitomir \u0160truc","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u0160truc, Vitomir","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009982931","display_name":"Haz\u0131m Kemal Ekenel","orcid":"https://orcid.org/0000-0003-3697-8548"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ekenel, Haz\u0131m Kemal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.7281000018119812,"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"}},"topics":[{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.7281000018119812,"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"}},{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.18400000035762787,"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":0.021400000900030136,"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/interpretability","display_name":"Interpretability","score":0.8740000128746033},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.6873999834060669},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.598800003528595},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.44749999046325684},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.38029998540878296},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.3797000050544739},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.34779998660087585},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.33340001106262207}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8740000128746033},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6873999834060669},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.649399995803833},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.598800003528595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5331000089645386},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5241000056266785},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5210000276565552},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.44749999046325684},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.38029998540878296},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.34779998660087585},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.33340001106262207},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.31619998812675476},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.2840999960899353},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.28139999508857727},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.25600001215934753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.17489","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17489","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.17489","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17489","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Face":[0],"Image":[1],"Quality":[2],"Assessment":[3],"(FIQA)":[4],"is":[5,61,200],"a":[6,83,88,107],"crucial":[7],"control":[8],"step":[9],"in":[10,50,82],"biometric":[11,131],"pipelines.":[12],"It":[13],"ensures":[14],"only":[15],"reliable":[16],"samples":[17],"are":[18,215],"processed":[19],"to":[20,75,115,125],"maintain":[21],"system":[22],"accuracy.":[23],"State-of-the-art":[24],"FIQA":[25,81,100,195,207],"methods":[26,101],"achieve":[27],"high":[28],"utility":[29,132],"but":[30],"typically":[31],"operate":[32],"as":[33,54,209],"\"black":[34],"boxes.\"":[35],"They":[36],"produce":[37],"scalar":[38],"scores":[39,162],"without":[40],"human-interpretable":[41],"justifications.":[42],"This":[43,96],"lack":[44],"of":[45,70,110,149],"transparency":[46],"limits":[47],"their":[48,120],"effectiveness":[49],"human-in-the-loop":[51],"scenarios,":[52],"such":[53],"automated":[55],"border":[56],"control,":[57],"where":[58],"actionable":[59],"feedback":[60],"essential.":[62],"In":[63],"this":[64,77],"paper,":[65],"we":[66,118],"investigate":[67],"the":[68,160,175],"potential":[69],"off-the-shelf":[71],"Vision-Language":[72],"Models":[73],"(VLMs)":[74],"bridge":[76],"gap":[78],"by":[79],"performing":[80],"zero-shot":[84,194],"setting.":[85],"We":[86,152],"present":[87],"comprehensive":[89],"evaluation":[90],"framework":[91],"for":[92],"assessing":[93],"VLM":[94,156],"performance.":[95],"involves":[97],"benchmarking":[98],"traditional":[99,150],"through":[102],"error-versus-reject":[103],"curves.":[104],"Additionally,":[105],"using":[106,198],"diverse":[108],"set":[109],"datasets,":[111],"ranging":[112],"from":[113],"surveillance-oriented":[114],"synthetically":[116],"generated,":[117],"analyzed":[119],"interpretability,":[121],"consistency,":[122,181],"and":[123,159,202],"robustness":[124],"prompt":[126],"changes.":[127],"Our":[128,167],"results":[129],"show":[130],"performance":[133,158,186],"depends":[134],"significantly":[135],"on":[136,140],"architecture,":[137],"not":[138],"merely":[139],"parameter":[141,176],"count.":[142],"Most":[143],"VLMs'":[144],"outputs":[145],"align":[146],"with":[147],"those":[148],"methods.":[151],"also":[153],"find":[154],"that":[155,172,193],"ranking":[157],"generated":[161],"may":[163],"vary":[164],"across":[165],"prompts.":[166],"synthetic":[168],"ablation":[169],"study":[170],"shows":[171],"while":[173],"increasing":[174],"count":[177],"can":[178],"improve":[179],"internal":[180],"it":[182],"yields":[183],"worse":[184],"degradation-detection":[185],"than":[187],"smaller":[188],"models.":[189],"These":[190],"findings":[191],"suggest":[192],"score":[196],"estimation":[197],"VLMs":[199],"promising":[201],"could":[203],"effectively":[204],"complement":[205],"conventional":[206],"pipelines":[208],"an":[210],"interpretability":[211],"module.":[212],"The":[213],"codes":[214],"available":[216],"at":[217],"https://github.com/ThEnded32/VLM4FIQA.git.":[218]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}
