{"id":"https://openalex.org/W4236338163","doi":"https://doi.org/10.1109/icip.2017.8296251","title":"Face anti-spoofing via deep local binary patterns","display_name":"Face anti-spoofing via deep local binary patterns","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W4236338163","doi":"https://doi.org/10.1109/icip.2017.8296251"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296251","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296251","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","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/A5101521759","display_name":"Lei Li","orcid":"https://orcid.org/0000-0003-4498-6126"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Li","raw_affiliation_strings":["Northwestern Polytechnical University, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101536090","display_name":"Xiaoyi Feng","orcid":"https://orcid.org/0000-0002-0428-6224"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyi Feng","raw_affiliation_strings":["Northwestern Polytechnical University, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051764820","display_name":"Zetao Jiang","orcid":"https://orcid.org/0000-0002-0914-2131"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyue Jiang","raw_affiliation_strings":["Northwestern Polytechnical University, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101410445","display_name":"Zhaoqiang Xia","orcid":"https://orcid.org/0000-0003-0630-3339"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoqiang Xia","raw_affiliation_strings":["Northwestern Polytechnical University, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013928164","display_name":"Abdenour Hadid","orcid":"https://orcid.org/0000-0001-9092-735X"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]},{"id":"https://openalex.org/I98381234","display_name":"University of Oulu","ror":"https://ror.org/03yj89h83","country_code":"FI","type":"education","lineage":["https://openalex.org/I98381234"]}],"countries":["CN","FI"],"is_corresponding":false,"raw_author_name":"Abdenour Hadid","raw_affiliation_strings":["Center for Machine Vision and Signal Analysis, University of Oulu, Finland","Northwestern Polytechnical University, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Machine Vision and Signal Analysis, University of Oulu, Finland","institution_ids":["https://openalex.org/I98381234"]},{"raw_affiliation_string":"Northwestern Polytechnical University, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.3616,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.93646786,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"101","last_page":"105"},"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":0.9889000058174133,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.98580002784729,"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":0.832344651222229},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7382421493530273},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7330989837646484},{"id":"https://openalex.org/keywords/local-binary-patterns","display_name":"Local binary patterns","score":0.7213571071624756},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7080832719802856},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.6322674751281738},{"id":"https://openalex.org/keywords/spoofing-attack","display_name":"Spoofing attack","score":0.6282252073287964},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6083866357803345},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.608113706111908},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5713772773742676},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5272690057754517},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4924639165401459},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4897017776966095},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.47209376096725464},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4409603178501129},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.43751275539398193},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3455007076263428},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.11808273196220398},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09962478280067444}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.832344651222229},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7382421493530273},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7330989837646484},{"id":"https://openalex.org/C87335442","wikidata":"https://www.wikidata.org/wiki/Q2494345","display_name":"Local binary patterns","level":4,"score":0.7213571071624756},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7080832719802856},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.6322674751281738},{"id":"https://openalex.org/C167900197","wikidata":"https://www.wikidata.org/wiki/Q11081100","display_name":"Spoofing attack","level":2,"score":0.6282252073287964},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6083866357803345},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.608113706111908},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5713772773742676},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5272690057754517},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4924639165401459},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4897017776966095},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.47209376096725464},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4409603178501129},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.43751275539398193},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3455007076263428},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.11808273196220398},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09962478280067444},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296251","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296251","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1487288055","https://openalex.org/W1704933117","https://openalex.org/W1770095230","https://openalex.org/W1982209341","https://openalex.org/W2009451935","https://openalex.org/W2039552902","https://openalex.org/W2106852298","https://openalex.org/W2118585731","https://openalex.org/W2136975665","https://openalex.org/W2163352848","https://openalex.org/W2163487272","https://openalex.org/W2174309130","https://openalex.org/W2341318667","https://openalex.org/W2578178601","https://openalex.org/W6637339757","https://openalex.org/W6677656871","https://openalex.org/W6684314024","https://openalex.org/W6700903540"],"related_works":["https://openalex.org/W2055219403","https://openalex.org/W2188559950","https://openalex.org/W2989910292","https://openalex.org/W2998478967","https://openalex.org/W3012240659","https://openalex.org/W1999808563","https://openalex.org/W1552490587","https://openalex.org/W3035701170","https://openalex.org/W2964083560","https://openalex.org/W3117807895"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"networks":[2],"(CNNs)":[3],"have":[4],"achieved":[5],"excellent":[6],"performance":[7,152],"in":[8,41,84,96],"the":[9,42,63,91,97,104,114,124],"field":[10],"of":[11,17,35,44,58,67,69,79,81,93],"pattern":[12],"recognition":[13],"when":[14,30],"huge":[15],"amount":[16,34],"training":[18,23,82],"data":[19,36,83],"is":[20,27,37,49],"available.":[21],"However,":[22],"a":[24,32],"CNN":[25,98],"model":[26],"less":[28],"obvious":[29],"only":[31],"limited":[33],"given":[38],"such":[39],"as":[40],"case":[43],"face":[45,85],"anti-spoofing":[46],"problem.":[47],"It":[48],"indeed":[50],"not":[51],"easy":[52],"to":[53,72,89,154],"collect":[54],"very":[55,150],"large":[56],"sets":[57],"fake":[59],"faces.":[60],"Especially":[61],"for":[62],"fully-connected":[64],"layers,":[65],"tens":[66],"thousands":[68],"parameters":[70],"need":[71],"be":[73],"learned.":[74],"To":[75],"tackle":[76],"this":[77],"problem":[78],"lack":[80],"anti-spoofing,":[86],"we":[87],"propose":[88],"explore":[90],"incorporation":[92],"hand-crafted":[94],"features":[95,110,128],"framework.":[99],"In":[100],"our":[101],"proposed":[102],"approach,":[103],"color":[105],"local":[106],"binary":[107],"patterns":[108],"(LBP)":[109],"are":[111,119,129,140],"extracted":[112],"from":[113],"convolutional":[115],"feature":[116],"maps,":[117],"which":[118],"fine":[120],"tuned":[121],"based":[122],"on":[123,142],"VGG-face":[125],"model.":[126],"These":[127],"then":[130],"fed":[131],"into":[132],"support":[133],"vector":[134],"machine":[135],"(SVM)":[136],"classifier.":[137],"Extensive":[138],"experiments":[139],"conducted":[141],"two":[143],"benchmark":[144],"and":[145],"publicly":[146],"available":[147],"databases":[148],"showing":[149],"interesting":[151],"compared":[153],"state-of-the-art":[155],"methods.":[156]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
