{"id":"https://openalex.org/W7133325544","doi":"https://doi.org/10.1109/ijcb65343.2025.11410756","title":"Unified Knowledge Distillation Framework: Fine-Grained Alignment and Geometric Relationship Preservation for Deep Face Recognition","display_name":"Unified Knowledge Distillation Framework: Fine-Grained Alignment and Geometric Relationship Preservation for Deep Face Recognition","publication_year":2025,"publication_date":"2025-09-08","ids":{"openalex":"https://openalex.org/W7133325544","doi":"https://doi.org/10.1109/ijcb65343.2025.11410756"},"language":null,"primary_location":{"id":"doi:10.1109/ijcb65343.2025.11410756","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb65343.2025.11410756","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Joint Conference on Biometrics (IJCB)","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/A5107841507","display_name":"Durgesh Kumar Mishra","orcid":null},"institutions":[{"id":"https://openalex.org/I288749910","display_name":"Indian Institute of Science Education and Research, Bhopal","ror":"https://ror.org/02rb21j89","country_code":"IN","type":"education","lineage":["https://openalex.org/I288749910"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Durgesh Mishra","raw_affiliation_strings":["Indian Institute of Science Education and Research Bhopal (IISER B)"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science Education and Research Bhopal (IISER B)","institution_ids":["https://openalex.org/I288749910"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5127959115","display_name":"Risabh Uikey","orcid":null},"institutions":[{"id":"https://openalex.org/I288749910","display_name":"Indian Institute of Science Education and Research, Bhopal","ror":"https://ror.org/02rb21j89","country_code":"IN","type":"education","lineage":["https://openalex.org/I288749910"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Risabh Uikey","raw_affiliation_strings":["Indian Institute of Science Education and Research Bhopal (IISER B)"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science Education and Research Bhopal (IISER B)","institution_ids":["https://openalex.org/I288749910"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5107841507"],"corresponding_institution_ids":["https://openalex.org/I288749910"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.77277855,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9125999808311462,"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":0.9125999808311462,"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/T10057","display_name":"Face and Expression Recognition","score":0.05139999836683273,"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":0.003800000064074993,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.73580002784729},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.6678000092506409},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6482999920845032},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6459000110626221},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5863000154495239},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5450999736785889},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4855000078678131},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.48170000314712524},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.43810001015663147}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.73580002784729},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7235999703407288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6722000241279602},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.6678000092506409},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6482999920845032},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6459000110626221},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5863000154495239},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5856000185012817},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5450999736785889},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4855000078678131},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.48170000314712524},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.43810001015663147},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43309998512268066},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4262999892234802},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41179999709129333},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3856000006198883},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.37779998779296875},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.267300009727478}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcb65343.2025.11410756","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb65343.2025.11410756","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Joint Conference on Biometrics (IJCB)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1988720110","https://openalex.org/W1998808035","https://openalex.org/W2096733369","https://openalex.org/W2121647436","https://openalex.org/W2138451337","https://openalex.org/W2138621090","https://openalex.org/W2145287260","https://openalex.org/W2165731615","https://openalex.org/W2194775991","https://openalex.org/W2325939864","https://openalex.org/W2341528187","https://openalex.org/W2543539599","https://openalex.org/W2606611007","https://openalex.org/W2663800299","https://openalex.org/W2736633948","https://openalex.org/W2752782242","https://openalex.org/W2871667416","https://openalex.org/W2884585870","https://openalex.org/W2948638722","https://openalex.org/W2962898354","https://openalex.org/W2963140444","https://openalex.org/W2963350250","https://openalex.org/W2963466847","https://openalex.org/W2963468606","https://openalex.org/W2963839617","https://openalex.org/W2969985801","https://openalex.org/W2986015886","https://openalex.org/W2998081460","https://openalex.org/W3034368386","https://openalex.org/W3035524453","https://openalex.org/W3110290422","https://openalex.org/W3169129566","https://openalex.org/W4312358144","https://openalex.org/W4312402191","https://openalex.org/W4312995938","https://openalex.org/W4382450293","https://openalex.org/W4386065714","https://openalex.org/W4402082644","https://openalex.org/W4404719528"],"related_works":[],"abstract_inverted_index":{"Knowledge":[0],"Distillation":[1,28,63,69],"(KD)":[2],"is":[3],"crucial":[4],"for":[5,10],"optimizing":[6],"face":[7,141],"recognition":[8,142],"models":[9],"deployment":[11],"in":[12],"computationally":[13],"limited":[14],"settings,":[15],"such":[16,23],"as":[17,24,144],"edge":[18],"devices.":[19],"Traditional":[20],"KD":[21,162],"methods,":[22],"Raw":[25],"L2":[26],"Feature":[27,30],"or":[29],"Consistency":[31],"(FC)":[32],"loss,":[33],"often":[34],"fail":[35],"to":[36,47,125,157,166],"capture":[37],"both":[38,113],"fine-grained":[39],"instance-level":[40,115],"details":[41],"and":[42,65,104,117],"complex":[43],"relational":[44,93],"structures,":[45],"leading":[46,124],"suboptimal":[48],"performance.":[49],"We":[50],"propose":[51],"a":[52,80,100,105,126],"unified":[53,110,132,161],"approach":[54],"that":[55],"integrates":[56],"two":[57],"novel":[58],"loss":[59],"functions:":[60],"Instance-Level":[61],"Embedding":[62],"(ILED)":[64],"Relation-Based":[66],"Pairwise":[67],"Similarity":[68],"(RPSD).":[70],"ILED":[71],"focuses":[72],"on":[73],"aligning":[74],"individual":[75],"feature":[76],"embeddings":[77],"by":[78,146],"leveraging":[79],"dynamic":[81],"hard":[82],"mining":[83,107],"strategy,":[84],"thereby":[85],"enhancing":[86],"learning":[87],"from":[88],"challenging":[89],"examples.":[90],"RPSD":[91],"captures":[92],"information":[94],"through":[95],"pairwise":[96],"similarity":[97],"relationships,":[98],"employing":[99],"memory":[101],"bank":[102],"mechanism":[103],"sample":[106],"strategy.":[108],"This":[109],"framework":[111,133],"ensures":[112],"effective":[114],"alignment":[116],"preservation":[118],"of":[119],"geometric":[120],"relationships":[121],"between":[122],"samples,":[123],"more":[127],"comprehensive":[128],"distillation":[129,136],"process.":[130],"Our":[131],"outperforms":[134],"state-of-the-art":[135],"methods":[137],"across":[138],"multiple":[139],"benchmark":[140],"datasets,":[143],"demonstrated":[145],"extensive":[147],"experimental":[148],"evaluations.":[149],"Interestingly,":[150],"when":[151],"using":[152],"strong":[153],"teacher":[154],"networks":[155],"compared":[156],"the":[158,164,169],"student,":[159],"our":[160],"enables":[163],"student":[165],"even":[167],"surpass":[168],"teacher\u2019s":[170],"accuracy.":[171]},"counts_by_year":[],"updated_date":"2026-03-05T07:30:30.508283","created_date":"2026-03-04T00:00:00"}
