{"id":"https://openalex.org/W4415345544","doi":"https://doi.org/10.1109/iccv51701.2025.01242","title":"Group-Wise Scaling and Orthogonal Decomposition for Domain-Invariant Feature Extraction in Face Anti-Spoofing","display_name":"Group-Wise Scaling and Orthogonal Decomposition for Domain-Invariant Feature Extraction in Face Anti-Spoofing","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4415345544","doi":"https://doi.org/10.1109/iccv51701.2025.01242"},"language":"en","primary_location":{"id":"doi:10.1109/iccv51701.2025.01242","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.01242","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2507.04006","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108824875","display_name":"Seungjin Jung","orcid":"https://orcid.org/0009-0009-7853-2588"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Seungjin Jung","raw_affiliation_strings":["Chung-Ang University,Department of Artificial Intelligence,Seoul,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Department of Artificial Intelligence,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101859847","display_name":"Kang-Hee Lee","orcid":"https://orcid.org/0009-0008-0398-1453"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kanghee Lee","raw_affiliation_strings":["Chung-Ang University,Department of Advanced Imaging,Seoul,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Department of Advanced Imaging,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003196471","display_name":"Yonghyun Jeong","orcid":null},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yonghyun Jeong","raw_affiliation_strings":["Naver Cloud,Seongnam,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Naver Cloud,Seongnam,Korea","institution_ids":["https://openalex.org/I60922564"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haeun Noh","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Haeun Noh","raw_affiliation_strings":["Chung-Ang University,Department of Artificial Intelligence,Seoul,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Department of Artificial Intelligence,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100375682","display_name":"Jungmin Lee","orcid":"https://orcid.org/0000-0002-8660-9444"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jungmin Lee","raw_affiliation_strings":["Chung-Ang University,Department of Advanced Imaging,Seoul,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Department of Advanced Imaging,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103250537","display_name":"Jong-Won Choi","orcid":"https://orcid.org/0000-0002-6146-9027"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongwon Choi","raw_affiliation_strings":["Chung-Ang University,Department of Artificial Intelligence,Seoul,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Department of Artificial Intelligence,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5108824875"],"corresponding_institution_ids":["https://openalex.org/I67900169"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33958801,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"13372","last_page":"13381"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9969000220298767,"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":0.9969000220298767,"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/T11448","display_name":"Face recognition and analysis","score":0.9581000208854675,"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/orthogonality","display_name":"Orthogonality","score":0.7128000259399414},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5809999704360962},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5656999945640564},{"id":"https://openalex.org/keywords/orthogonalization","display_name":"Orthogonalization","score":0.5313000082969666},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5159000158309937},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5101000070571899},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4993000030517578},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48820000886917114},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.4832000136375427}],"concepts":[{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.7128000259399414},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6439999938011169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6427000164985657},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5809999704360962},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5656999945640564},{"id":"https://openalex.org/C47559304","wikidata":"https://www.wikidata.org/wiki/Q1702189","display_name":"Orthogonalization","level":2,"score":0.5313000082969666},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5159000158309937},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5101000070571899},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4993000030517578},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48820000886917114},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4832000136375427},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.482699990272522},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.475600004196167},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.45320001244544983},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4410000145435333},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4250999987125397},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3919999897480011},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.39100000262260437},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.3366999924182892},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3255999982357025},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3228999972343445},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.31709998846054077},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.26570001244544983}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iccv51701.2025.01242","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.01242","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2507.04006","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.04006","pdf_url":"https://arxiv.org/pdf/2507.04006","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2507.04006","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.04006","pdf_url":"https://arxiv.org/pdf/2507.04006","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"Domain":[0],"Generalizable":[1],"Face":[2],"Anti-Spoofing":[3],"(DGFAS)":[4],"methods":[5],"effectively":[6],"capture":[7],"domain-invariant":[8,95,105],"features":[9,106],"by":[10,75],"aligning":[11,145],"the":[12,22,84,90,139],"directions":[13],"(weights)":[14],"of":[15,141],"local":[16],"decision":[17],"boundaries":[18,28],"across":[19,79,117,148],"domains.":[20,42,81,149,175],"However,":[21],"bias":[23,73,122,146,166],"terms":[24,147],"associated":[25],"with":[26],"these":[27],"remain":[29],"misaligned,":[30],"leading":[31],"to":[32,88],"inconsistent":[33],"classification":[34],"thresholds":[35],"and":[36,57,64,96,104,168],"degraded":[37],"performance":[38],"on":[39,152,172],"unseen":[40,173],"target":[41,174],"To":[43],"address":[44],"this":[45],"issue,":[46],"we":[47,125],"propose":[48],"a":[49,132],"novel":[50,133],"DGFAS":[51],"framework":[52],"that":[53,156],"jointly":[54],"aligns":[55],"weights":[56],"biases":[58],"through":[59],"Feature":[60],"Orthogonal":[61],"Decomposition":[62],"(FOD)":[63],"Group-wise":[65],"Scaling":[66],"Risk":[67],"Minimization":[68],"(GS-RM).":[69],"Specifically,":[70],"GS-RM":[71],"facilitates":[72],"alignment":[74,116],"balancing":[76],"group-wise":[77],"losses":[78],"multiple":[80],"FOD":[82,112],"employs":[83],"Gram-Schmidt":[85],"orthogonalization":[86],"process":[87],"decompose":[89],"feature":[91],"space":[92],"explicitly":[93],"into":[94],"domain-specific":[97,103],"subspaces.":[98],"By":[99],"enforcing":[100],"orthogonality":[101],"between":[102],"during":[107],"training":[108],"using":[109],"domain":[110],"labels,":[111],"ensures":[113],"effective":[114],"weight":[115],"domains":[118],"without":[119],"negatively":[120],"impacting":[121],"alignment.":[123],"Additionally,":[124],"introduce":[126],"Expected":[127],"Calibration":[128],"Error":[129],"(ECE)":[130],"as":[131],"evaluation":[134],"metric":[135],"for":[136],"quantitatively":[137],"assessing":[138],"effectiveness":[140],"our":[142,157],"method":[143],"in":[144],"Extensive":[150],"experiments":[151],"benchmark":[153],"datasets":[154],"demonstrate":[155],"approach":[158],"achieves":[159],"state-of-the-art":[160],"performance,":[161],"consistently":[162],"improving":[163],"accuracy,":[164],"reducing":[165],"misalignment,":[167],"enhancing":[169],"generalization":[170],"stability":[171]},"counts_by_year":[],"updated_date":"2026-05-06T06:03:25.996018","created_date":"2025-10-20T00:00:00"}
