{"id":"https://openalex.org/W3157512872","doi":"https://doi.org/10.1109/icpr48806.2021.9413163","title":"SoftmaxOut Transformation-Permutation Network for Facial Template Protection","display_name":"SoftmaxOut Transformation-Permutation Network for Facial Template Protection","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3157512872","doi":"https://doi.org/10.1109/icpr48806.2021.9413163","mag":"3157512872"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9413163","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413163","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5088863775","display_name":"Hakyoung Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hakyoung Lee","raw_affiliation_strings":["School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083277795","display_name":"Cheng-Yaw Low","orcid":"https://orcid.org/0000-0002-6764-0614"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Cheng Yaw Low","raw_affiliation_strings":["School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051093782","display_name":"Andrew Beng Jin Teoh","orcid":"https://orcid.org/0000-0001-5063-9484"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Andrew Beng Jin Teoh","raw_affiliation_strings":["School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7558","last_page":"7565"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9998000264167786,"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/T10388","display_name":"Advanced Steganography and Watermarking Techniques","score":0.9970999956130981,"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":0.7058192491531372},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5373588800430298},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.5195163488388062},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5127097368240356},{"id":"https://openalex.org/keywords/permutation","display_name":"Permutation (music)","score":0.5099631547927856},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.46309661865234375},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.4373045861721039},{"id":"https://openalex.org/keywords/handwriting","display_name":"Handwriting","score":0.42736032605171204},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.41568344831466675},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.33253157138824463}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7058192491531372},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5373588800430298},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.5195163488388062},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5127097368240356},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.5099631547927856},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.46309661865234375},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.4373045861721039},{"id":"https://openalex.org/C2779386606","wikidata":"https://www.wikidata.org/wiki/Q2393642","display_name":"Handwriting","level":2,"score":0.42736032605171204},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.41568344831466675},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33253157138824463},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr48806.2021.9413163","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413163","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.699999988079071,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G7238793834","display_name":null,"funder_award_id":"NRF-2019R1A2C1003306","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1490122681","https://openalex.org/W1782590233","https://openalex.org/W2019464758","https://openalex.org/W2024922353","https://openalex.org/W2062672786","https://openalex.org/W2063355793","https://openalex.org/W2101251249","https://openalex.org/W2103919449","https://openalex.org/W2104186330","https://openalex.org/W2110401929","https://openalex.org/W2110990863","https://openalex.org/W2121949863","https://openalex.org/W2508837377","https://openalex.org/W2755909956","https://openalex.org/W2777595189","https://openalex.org/W2784163702","https://openalex.org/W2790072426","https://openalex.org/W2798233603","https://openalex.org/W2886065796","https://openalex.org/W2895705849","https://openalex.org/W2900795375","https://openalex.org/W2902696888","https://openalex.org/W2910902729","https://openalex.org/W2963466847","https://openalex.org/W2965379436","https://openalex.org/W2969985801","https://openalex.org/W2989733199","https://openalex.org/W3035496054","https://openalex.org/W3083147215","https://openalex.org/W3083350787","https://openalex.org/W3103152812","https://openalex.org/W3104110632","https://openalex.org/W4210880854","https://openalex.org/W6725199262"],"related_works":["https://openalex.org/W3107204728","https://openalex.org/W4287591324","https://openalex.org/W4226420367","https://openalex.org/W2980176872","https://openalex.org/W2962876041","https://openalex.org/W3090555870","https://openalex.org/W3095152779","https://openalex.org/W3119773509","https://openalex.org/W3128220219","https://openalex.org/W3006353185"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,20,38,70,76],"data-driven":[6],"cancellable":[7],"biometrics":[8],"scheme,":[9],"referred":[10],"to":[11,53,83],"as":[12],"SoftmaxOut":[13,40,104],"Transformation-Permutation":[14],"Network":[15],"(SOTPN).":[16],"The":[17,106],"SOTPN":[18,108,128],"is":[19,90,109],"neural":[21],"version":[22],"of":[23,68],"Random":[24],"Permutation":[25],"Maxout":[26],"(RPM)":[27],"transform,":[28],"which":[29],"was":[30],"introduced":[31],"for":[32],"facial":[33,88],"template":[34,89],"protection.":[35],"We":[36],"present":[37],"specialized":[39],"layer":[41],"integrated":[42],"with":[43,102],"the":[44,49,55,59,63,86,103,127,130],"permutable":[45],"MaxOut":[46],"units":[47],"and":[48,58,75,95,119,121],"parameterized":[50],"softmax":[51],"function":[52],"approximate":[54],"nondifferentiable":[56],"permutation":[57],"winner-takes-all":[60],"operations":[61],"in":[62],"RPM":[64,131],"transform.":[65],"On":[66],"top":[67],"that,":[69],"novel":[71],"pairwise":[72],"ArcFace":[73],"loss":[74,79],"code":[77],"balancing":[78],"are":[80],"also":[81],"formulated":[82],"ensure":[84],"that":[85,126],"SOTPN-transformed":[87],"cancellable,":[91],"discriminative,":[92],"high":[93],"entropy":[94],"free":[96],"from":[97],"quantization":[98],"errors":[99],"when":[100],"coupled":[101],"layer.":[105],"proposed":[107],"evaluated":[110],"on":[111],"three":[112],"face":[113],"datasets,":[114],"namely":[115],"LFW,":[116],"YouTube":[117],"Face":[118],"Facescrub,":[120],"our":[122],"experimental":[123],"results":[124],"disclosed":[125],"outperforms":[129],"transform":[132],"significantly.":[133]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
