{"id":"https://openalex.org/W2760126305","doi":"https://doi.org/10.1109/iscas.2017.8050421","title":"Palmprint recognition using deep scattering network","display_name":"Palmprint recognition using deep scattering network","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2760126305","doi":"https://doi.org/10.1109/iscas.2017.8050421","mag":"2760126305"},"language":"en","primary_location":{"id":"doi:10.1109/iscas.2017.8050421","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2017.8050421","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Symposium on Circuits and Systems (ISCAS)","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/A5025412119","display_name":"Shrevin Minaee","orcid":null},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shrevin Minaee","raw_affiliation_strings":["Electrical and Computer Engineering Department, New York University, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, New York University, USA","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100319039","display_name":"Yao Wang","orcid":"https://orcid.org/0000-0003-3199-3802"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yao Wang","raw_affiliation_strings":["Electrical and Computer Engineering Department, New York University, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, New York University, USA","institution_ids":["https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5025412119"],"corresponding_institution_ids":["https://openalex.org/I57206974"],"apc_list":null,"apc_paid":null,"fwci":5.1847,"has_fulltext":false,"cited_by_count":53,"citation_normalized_percentile":{"value":0.96458104,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":1.0,"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":1.0,"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/T10057","display_name":"Face and Expression Recognition","score":0.9850999712944031,"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/T13192","display_name":"Forensic Fingerprint Detection Methods","score":0.9782000184059143,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7950490117073059},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7938156127929688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6836682558059692},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6169862747192383},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.569591760635376},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.569178581237793},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.5460938215255737},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5335121154785156},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5296789407730103},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5077646970748901},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5025746822357178},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.4936167001724243},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4844382107257843},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4162783920764923},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.3645630478858948},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.335030198097229},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19615361094474792},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16403242945671082},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.10754302144050598}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7950490117073059},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7938156127929688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6836682558059692},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6169862747192383},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.569591760635376},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.569178581237793},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.5460938215255737},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5335121154785156},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5296789407730103},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5077646970748901},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5025746822357178},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4936167001724243},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4844382107257843},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4162783920764923},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.3645630478858948},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.335030198097229},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19615361094474792},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16403242945671082},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10754302144050598},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas.2017.8050421","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2017.8050421","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322598","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W175058090","https://openalex.org/W1968113673","https://openalex.org/W1974900975","https://openalex.org/W1995109806","https://openalex.org/W2003289302","https://openalex.org/W2046182649","https://openalex.org/W2068424163","https://openalex.org/W2072072671","https://openalex.org/W2080033855","https://openalex.org/W2087165947","https://openalex.org/W2119821739","https://openalex.org/W2128728535","https://openalex.org/W2151103935","https://openalex.org/W2153635508","https://openalex.org/W2160776381","https://openalex.org/W2163605009","https://openalex.org/W2167383966","https://openalex.org/W2282078507","https://openalex.org/W2404938947","https://openalex.org/W2963604920","https://openalex.org/W4239510810","https://openalex.org/W6684191040","https://openalex.org/W6713805741"],"related_works":["https://openalex.org/W3034955165","https://openalex.org/W2094920358","https://openalex.org/W2041448692","https://openalex.org/W2247121321","https://openalex.org/W2391926582","https://openalex.org/W1966831329","https://openalex.org/W2316074893","https://openalex.org/W2020188645","https://openalex.org/W2739923608","https://openalex.org/W2087391438"],"abstract_inverted_index":{"Palmprint":[0],"recognition":[1,20,131],"has":[2,146],"drawn":[3],"a":[4,38,54,118,150],"lot":[5],"of":[6,32,92,102,121,158],"attentions":[7],"during":[8],"recent":[9],"years.":[10],"Different":[11],"features":[12,73,87],"and":[13,30,60,83,140,154,160,166],"algorithms":[14,171],"have":[15],"been":[16,147],"proposed":[17,144],"for":[18,49],"palmprint":[19,152],"in":[21,74,98],"the":[22,72,84,93,128,130],"past":[23],"such":[24,70],"as":[25],"Gabor-based":[26],"features,":[27,29,104],"wavelet":[28,64],"histogram":[31],"oriented":[33],"lines.":[34],"In":[35],"this":[36,173],"paper,":[37],"powerful":[39],"image":[40],"representation,":[41],"so":[42],"called":[43],"deep":[44],"scattering":[45,103],"network,":[46],"is":[47,53,68,107,132],"used":[48],"recognition.":[50],"Scattering":[51,66],"network":[52,56],"convolutional":[55],"where":[57],"its":[58,75],"architecture":[59],"filters":[61],"are":[62,78,96],"predefined":[63],"transforms.":[65],"transform":[67],"designed":[69],"that":[71],"first":[76],"layer":[77],"similar":[79],"to":[80],"SIFT":[81],"descriptors":[82],"higher":[85,89],"layers'":[86],"capture":[88],"frequency":[90],"content":[91],"signal":[94],"which":[95],"lost":[97],"SIFT.":[99],"After":[100],"extraction":[101],"their":[105],"dimensionality":[106],"reduced":[108],"by":[109],"applying":[110],"principal":[111],"component":[112],"analysis":[113],"(PCA).":[114],"By":[115],"doing":[116],"so,":[117],"great":[119],"amount":[120],"computation":[122],"complexity":[123],"can":[124],"be":[125],"reduced.":[126],"At":[127],"end,":[129],"performed":[133],"using":[134,162],"two":[135],"different":[136],"classifiers,":[137],"multi-class":[138],"SVM":[139,167],"minimum-distance":[141],"classifier.":[142],"The":[143],"scheme":[145],"tested":[148],"on":[149,172],"well-known":[151],"database":[153],"achieved":[155],"accuracy":[156],"rates":[157],"99.4%":[159],"99.9%":[161],"minimum":[163],"distance":[164],"classifier":[165],"respectively,":[168],"outperforming":[169],"previous":[170],"dataset.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":15},{"year":2018,"cited_by_count":7}],"updated_date":"2026-02-28T09:26:25.869077","created_date":"2025-10-10T00:00:00"}