{"id":"https://openalex.org/W2396462482","doi":"https://doi.org/10.1201/b18050-25","title":"\u25fe Big Data Biometrics Processing: A Case Study of an Iris Matching Algorithm on Intel Xeon Phi","display_name":"\u25fe Big Data Biometrics Processing: A Case Study of an Iris Matching Algorithm on Intel Xeon Phi","publication_year":2015,"publication_date":"2015-02-23","ids":{"openalex":"https://openalex.org/W2396462482","doi":"https://doi.org/10.1201/b18050-25","mag":"2396462482"},"language":"en","primary_location":{"id":"doi:10.1201/b18050-25","is_oa":false,"landing_page_url":"https://doi.org/10.1201/b18050-25","pdf_url":null,"source":{"id":"https://openalex.org/S4210226731","display_name":"Big Data","issn_l":"2167-6461","issn":["2167-6461","2167-647X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320443","host_organization_name":"Mary Ann Liebert, Inc.","host_organization_lineage":["https://openalex.org/P4310320443"],"host_organization_lineage_names":["Mary Ann Liebert, Inc."],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.17727273,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"430","last_page":"441"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9742000102996826,"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":0.9742000102996826,"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/xeon-phi","display_name":"Xeon Phi","score":0.7185801267623901},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.7142493724822998},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6481842994689941},{"id":"https://openalex.org/keywords/iris","display_name":"IRIS (biosensor)","score":0.5912764072418213},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.450679749250412},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.423764705657959},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38862279057502747},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.357735812664032},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1770138144493103},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.05320456624031067}],"concepts":[{"id":"https://openalex.org/C96972482","wikidata":"https://www.wikidata.org/wiki/Q1049168","display_name":"Xeon Phi","level":2,"score":0.7185801267623901},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.7142493724822998},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6481842994689941},{"id":"https://openalex.org/C2779503344","wikidata":"https://www.wikidata.org/wiki/Q5973514","display_name":"IRIS (biosensor)","level":3,"score":0.5912764072418213},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.450679749250412},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.423764705657959},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38862279057502747},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.357735812664032},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1770138144493103},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.05320456624031067}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1201/b18050-25","is_oa":false,"landing_page_url":"https://doi.org/10.1201/b18050-25","pdf_url":null,"source":{"id":"https://openalex.org/S4210226731","display_name":"Big Data","issn_l":"2167-6461","issn":["2167-6461","2167-647X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320443","host_organization_name":"Mary Ann Liebert, Inc.","host_organization_lineage":["https://openalex.org/P4310320443"],"host_organization_lineage_names":["Mary Ann Liebert, Inc."],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W57243869"],"related_works":["https://openalex.org/W2981664121","https://openalex.org/W2526069705","https://openalex.org/W2198906585","https://openalex.org/W2773471004","https://openalex.org/W2024016913","https://openalex.org/W1551178083","https://openalex.org/W88591960","https://openalex.org/W2035419609","https://openalex.org/W3090309567","https://openalex.org/W2214459866"],"abstract_inverted_index":{"ABSTRACT":[0],"With":[1],"the":[2,48,52,57,60,77,148,151,175,182,196,199,204,210,213],"rapidly":[3],"expanded":[4],"biometric":[5,31],"data":[6],"collected":[7],"by":[8],"various":[9],"sources":[10],"for":[11],"identi-":[12],"cation":[13],"and":[14,20,41,64,195,209],"verication":[15],"purposes,":[16],"how":[17],"to":[18,177,190],"manage":[19],"process":[21],"such":[22],"Big":[23,85],"Data":[24,86],"draws":[25],"great":[26],"concern.":[27],"On":[28,56],"one":[29],"hand,":[30,59],"applications":[32,152],"normally":[33],"involve":[34],"comparing":[35],"a":[36,154,164],"huge":[37],"amount":[38],"of":[39,51,62,125,132,150,174,202,206,212],"samples":[40],"templates,":[42],"which":[43],"has":[44,71],"strict":[45],"requirements":[46],"on":[47,181],"computational":[49],"capability":[50],"underlying":[53],"hardware":[54,68,214],"platform.":[55],"other":[58],"number":[61,201],"cores":[63],"associated":[65],"threads":[66],"that":[67],"can":[69],"support":[70],"increased":[72],"greatly;":[73],"an":[74,159,169],"example":[75],"is":[76,189],"newly":[78],"released":[79],"Intel":[80,95,108,183],"Xeon":[81,96,184],"Phi":[82,97,185],"coprocessor.":[83,186],"Hence,":[84],"CONTENTS":[87],"20.1":[88],"Introduction":[89],"394":[90],"20.2":[91],"Background":[92],"395":[93,98],"20.2.1":[94],"20.2.2":[99],"Iris":[100],"Matching":[101],"Algorithm":[102],"396":[103],"20.2.3":[104],"OpenMP":[105],"397":[106,111],"20.2.4":[107],"VTune":[109],"Amplier":[110],"20.3":[112],"Experiments":[113],"398":[114,118,122],"20.3.1":[115],"Experiment":[116],"Setup":[117],"20.3.2":[119],"Workload":[120],"Characteristics":[121],"20.3.3":[123],"Impact":[124],"Dierent":[126],"Anity":[127],"399":[128],"20.3.4":[129],"Optimal":[130],"Number":[131],"reads":[133],"401":[134,137],"20.3.5":[135],"Vectorization":[136],"20.4":[138],"Conclusions":[139],"403":[140,142,144],"Acknowledgments":[141],"References":[143],"biometrics":[145],"processing":[146],"demands":[147],"execution":[149],"at":[153],"higher":[155],"parallelism":[156],"level.":[157],"Taking":[158],"iris":[160],"matching":[161],"algorithm":[162,176],"as":[163],"case":[165],"study,":[166],"we":[167],"implemented":[168],"open":[170],"multi-processing":[171],"(OpenMP)":[172],"version":[173],"examine":[178],"its":[179],"performance":[180],"Our":[187],"target":[188],"evaluate":[191],"our":[192],"parallelization":[193],"approach":[194],"inuence":[197],"from":[198],"optimal":[200],"threads,":[203],"impact":[205,211],"thread-to-core":[207],"anity,":[208],"vector":[215],"engine.":[216]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}
