{"id":"https://openalex.org/W2903036973","doi":"https://doi.org/10.23919/biosig.2018.8553003","title":"Deep Sparse Feature Selection and Fusion for Textured Contact Lens Detection","display_name":"Deep Sparse Feature Selection and Fusion for Textured Contact Lens Detection","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2903036973","doi":"https://doi.org/10.23919/biosig.2018.8553003","mag":"2903036973"},"language":"en","primary_location":{"id":"doi:10.23919/biosig.2018.8553003","is_oa":false,"landing_page_url":"https://doi.org/10.23919/biosig.2018.8553003","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference of the Biometrics Special Interest Group (BIOSIG)","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/A5011391575","display_name":"Domenick Poster","orcid":"https://orcid.org/0000-0001-5670-3651"},"institutions":[{"id":"https://openalex.org/I12097938","display_name":"West Virginia University","ror":"https://ror.org/011vxgd24","country_code":"US","type":"education","lineage":["https://openalex.org/I12097938"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Domenick Poster","raw_affiliation_strings":["Lane Dept. of Computer Science and Electrical Engineering, West Virginia University, Morgantown, West Virginia"],"affiliations":[{"raw_affiliation_string":"Lane Dept. of Computer Science and Electrical Engineering, West Virginia University, Morgantown, West Virginia","institution_ids":["https://openalex.org/I12097938"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021852735","display_name":"Nasser M. Nasrabadi","orcid":"https://orcid.org/0000-0001-8730-627X"},"institutions":[{"id":"https://openalex.org/I12097938","display_name":"West Virginia University","ror":"https://ror.org/011vxgd24","country_code":"US","type":"education","lineage":["https://openalex.org/I12097938"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nasser Nasrabadi","raw_affiliation_strings":["Lane Dept. of Computer Science and Electrical Engineering, West Virginia University, Morgantown, West Virginia"],"affiliations":[{"raw_affiliation_string":"Lane Dept. of Computer Science and Electrical Engineering, West Virginia University, Morgantown, West Virginia","institution_ids":["https://openalex.org/I12097938"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075289214","display_name":"Benjamin S. Riggan","orcid":"https://orcid.org/0000-0003-2293-0439"},"institutions":[{"id":"https://openalex.org/I166416128","display_name":"DEVCOM Army Research Laboratory","ror":"https://ror.org/011hc8f90","country_code":"US","type":"government","lineage":["https://openalex.org/I1304082316","https://openalex.org/I1330347796","https://openalex.org/I166416128","https://openalex.org/I2802705668","https://openalex.org/I4210154437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Benjamin Riggan","raw_affiliation_strings":["US Army Research Laboratory Adelphi, Maryland"],"affiliations":[{"raw_affiliation_string":"US Army Research Laboratory Adelphi, Maryland","institution_ids":["https://openalex.org/I166416128"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5011391575"],"corresponding_institution_ids":["https://openalex.org/I12097938"],"apc_list":null,"apc_paid":null,"fwci":2.0571,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.89481335,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9835000038146973,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9835000038146973,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11194","display_name":"Ocular Diseases and Beh\u00e7et\u2019s Syndrome","score":0.9743000268936157,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.973800003528595,"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/overfitting","display_name":"Overfitting","score":0.82149738073349},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7751405239105225},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7567318677902222},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6125154495239258},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.49443602561950684},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.4911341369152069},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.48321521282196045},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48052701354026794},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4710780680179596},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.47066009044647217},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.4348950982093811},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4264585077762604},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4241655468940735},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3643456697463989},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2969669699668884},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23069897294044495}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.82149738073349},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7751405239105225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7567318677902222},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6125154495239258},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49443602561950684},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.4911341369152069},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.48321521282196045},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48052701354026794},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4710780680179596},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.47066009044647217},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.4348950982093811},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4264585077762604},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4241655468940735},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3643456697463989},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2969669699668884},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23069897294044495},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/biosig.2018.8553003","is_oa":false,"landing_page_url":"https://doi.org/10.23919/biosig.2018.8553003","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference of the Biometrics Special Interest Group (BIOSIG)","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":31,"referenced_works":["https://openalex.org/W132579780","https://openalex.org/W1502160187","https://openalex.org/W1923137425","https://openalex.org/W2011475289","https://openalex.org/W2015159529","https://openalex.org/W2018522351","https://openalex.org/W2068779413","https://openalex.org/W2083231836","https://openalex.org/W2095705004","https://openalex.org/W2101234009","https://openalex.org/W2104853049","https://openalex.org/W2136975665","https://openalex.org/W2142256325","https://openalex.org/W2150817856","https://openalex.org/W2161969291","https://openalex.org/W2163352848","https://openalex.org/W2286063915","https://openalex.org/W2460144244","https://openalex.org/W2563133041","https://openalex.org/W2627044814","https://openalex.org/W2736967564","https://openalex.org/W2787293095","https://openalex.org/W2798526793","https://openalex.org/W2963000224","https://openalex.org/W2964009128","https://openalex.org/W3101824741","https://openalex.org/W6605310329","https://openalex.org/W6630005630","https://openalex.org/W6652930495","https://openalex.org/W6674330103","https://openalex.org/W6675354045"],"related_works":["https://openalex.org/W4298017035","https://openalex.org/W3110700750","https://openalex.org/W2792147139","https://openalex.org/W2998675825","https://openalex.org/W4226354336","https://openalex.org/W3128220493","https://openalex.org/W2736804899","https://openalex.org/W2897443685","https://openalex.org/W4307654087","https://openalex.org/W2951100320"],"abstract_inverted_index":{"Distinguishing":[0],"between":[1],"images":[2],"of":[3,49,71,130,143],"irises":[4],"wearing":[5,10],"textured":[6,114],"lenses":[7,12,15],"versus":[8],"those":[9],"transparent":[11],"or":[13,146],"no":[14],"is":[16],"a":[17,52,80,86,92,102],"challenging":[18],"problem":[19,147],"due":[20],"to":[21,61,135,138],"the":[22,45,62,68,109,119,127,131],"subtle":[23],"and":[24,36,43,66,82,91,117],"fine-grained":[25],"visual":[26],"differences.":[27],"Our":[28],"approach":[29],"builds":[30],"upon":[31],"existing":[32],"hand-crafted":[33],"image":[34,140],"features":[35,50,72],"neural":[37],"network":[38],"architectures":[39],"by":[40],"optimally":[41],"selecting":[42],"combining":[44],"most":[46],"useful":[47],"set":[48],"into":[51],"single":[53],"model.":[54],"We":[55,76],"build":[56],"multiple,":[57],"parallel":[58],"subnetworks":[59],"corresponding":[60],"various":[63],"feature":[64],"descriptors":[65],"learn":[67],"best":[69],"subset":[70],"through":[73,85],"group":[74,94],"sparsity.":[75],"avoid":[77],"overfitting":[78],"such":[79],"wide":[81],"deep":[83],"model":[84,99],"selective":[87],"transfer":[88],"learning":[89],"technique":[90],"novel":[93],"Dropout":[95],"regularization":[96],"strategy.":[97],"This":[98],"achieves":[100],"roughly":[101],"four":[103],"times":[104],"increase":[105],"in":[106],"performance":[107],"over":[108],"state-of-the-art":[110,121],"on":[111,123],"three":[112],"benchmark":[113],"lens":[115],"datasets":[116],"equals":[118],"near-perfect":[120],"accuracy":[122],"two":[124],"others.":[125],"Furthermore,":[126],"generic":[128],"nature":[129],"architecture":[132],"allows":[133],"it":[134],"be":[136],"extended":[137],"other":[139],"features,":[141],"forms":[142],"spoofing":[144],"attacks,":[145],"domains.":[148]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
