{"id":"https://openalex.org/W2353416844","doi":"https://doi.org/10.1109/icics.2015.7459973","title":"Latent fingerprint orientation estimation via sparse representation","display_name":"Latent fingerprint orientation estimation via sparse representation","publication_year":2015,"publication_date":"2015-12-01","ids":{"openalex":"https://openalex.org/W2353416844","doi":"https://doi.org/10.1109/icics.2015.7459973","mag":"2353416844"},"language":"en","primary_location":{"id":"doi:10.1109/icics.2015.7459973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icics.2015.7459973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 10th International Conference on Information, Communications and Signal Processing (ICICS)","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/A5079985761","display_name":"Manhua Liu","orcid":"https://orcid.org/0000-0001-6496-670X"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Manhua Liu","raw_affiliation_strings":["Department of Instrument Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Instrument Science and Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100775925","display_name":"Shuxin Liu","orcid":"https://orcid.org/0000-0002-3459-7330"},"institutions":[{"id":"https://openalex.org/I9356336","display_name":"Zhangzhou Normal University","ror":"https://ror.org/02vj1vm13","country_code":"CN","type":"education","lineage":["https://openalex.org/I9356336"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuxin Liu","raw_affiliation_strings":["School of Educational Science, Minnan Normal University, Fujian, China"],"affiliations":[{"raw_affiliation_string":"School of Educational Science, Minnan Normal University, Fujian, China","institution_ids":["https://openalex.org/I9356336"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5079985761"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.2047694,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"102","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":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/T12357","display_name":"Digital Media Forensic Detection","score":0.9926000237464905,"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/T13568","display_name":"Wood and Agarwood Research","score":0.982699990272522,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.7586063146591187},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7451289892196655},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.730541467666626},{"id":"https://openalex.org/keywords/fingerprint","display_name":"Fingerprint (computing)","score":0.6991056203842163},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6863279342651367},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.6444628834724426},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5011520385742188},{"id":"https://openalex.org/keywords/fingerprint-recognition","display_name":"Fingerprint recognition","score":0.4547748267650604},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37635964155197144},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19372591376304626}],"concepts":[{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.7586063146591187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7451289892196655},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.730541467666626},{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.6991056203842163},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6863279342651367},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.6444628834724426},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5011520385742188},{"id":"https://openalex.org/C168406668","wikidata":"https://www.wikidata.org/wiki/Q178022","display_name":"Fingerprint recognition","level":3,"score":0.4547748267650604},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37635964155197144},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19372591376304626},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icics.2015.7459973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icics.2015.7459973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 10th International Conference on Information, Communications and Signal Processing (ICICS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1639795441","https://openalex.org/W1964320810","https://openalex.org/W1970693754","https://openalex.org/W2095773423","https://openalex.org/W2113606819","https://openalex.org/W2123597619","https://openalex.org/W2125511134","https://openalex.org/W2127717018","https://openalex.org/W2133719507","https://openalex.org/W2137441863","https://openalex.org/W2145933558","https://openalex.org/W2153663612","https://openalex.org/W2161160262","https://openalex.org/W2170075141","https://openalex.org/W2488267379","https://openalex.org/W2494247973","https://openalex.org/W6676903177"],"related_works":["https://openalex.org/W3014822659","https://openalex.org/W2117826006","https://openalex.org/W4362496757","https://openalex.org/W2051501574","https://openalex.org/W2124627279","https://openalex.org/W2050967184","https://openalex.org/W2566091814","https://openalex.org/W2114937328","https://openalex.org/W2148654711","https://openalex.org/W2608025327"],"abstract_inverted_index":{"The":[0,84,148],"local":[1],"ridge":[2,10,48],"orientations":[3,36],"are":[4,41,93,213],"often":[5],"used":[6,171],"to":[7,32,105,138,195,215],"describe":[8],"fingerprint":[9,17,142,169,211],"flow":[11],"patterns,":[12,113],"providing":[13],"useful":[14],"information":[15],"for":[16,25,37,81,172],"recognition.":[18],"Although":[19],"significant":[20],"advances":[21],"have":[22],"been":[23],"achieved":[24],"orientation":[26,71,99,112,121,173,178,198],"estimation,":[27],"it":[28],"is":[29,117,135,156,170,180,193],"still":[30],"challenging":[31],"reliably":[33],"compute":[34],"the":[35,56,102,107,120,128,131,140,159,162,167,176,183,190,197,217,220],"latent":[38,82,141,201,210],"fingerprints,":[39],"which":[40,92],"usually":[42],"of":[43,59,88,110,123,153,166,200,219],"poor":[44],"quality":[45,125],"with":[46,151,182],"unclear":[47],"structure":[49],"and":[50,65,78,97,145,186,205],"various":[51,111],"overlapping":[52],"patterns.":[53],"Motivated":[54],"by":[55],"recent":[57],"success":[58],"sparse":[60,79,187],"representation":[61,80,188],"in":[62],"image":[63],"denoising":[64],"reconstruction,":[66],"this":[67],"paper":[68],"proposes":[69],"an":[70],"estimation":[72],"algorithm":[73,86],"based":[74],"on":[75,189,207],"dictionary":[76,95,116,192],"learning":[77,96],"fingerprints.":[83,126],"proposed":[85,221],"consists":[87],"two":[89],"main":[90],"stages":[91],"offline":[94,103],"online":[98,129],"estimation.":[100,174],"In":[101,127],"stage,":[104,130],"capture":[106],"prior":[108],"knowledge":[109],"a":[114],"redundant":[115,191],"learned":[118],"from":[119],"fields":[122],"good":[124],"total":[132],"variation":[133],"model":[134],"first":[136],"applied":[137],"decompose":[139],"into":[143],"cartoon":[144,149],"texture":[146,163],"components.":[147],"component":[150,164],"most":[152],"non-fingerprint":[154],"patterns":[155],"removed":[157],"as":[158],"noise,":[160],"while":[161],"consisting":[165],"weak":[168],"Second,":[175],"initial":[177],"field":[179,199],"estimated":[181],"traditional":[184],"method":[185],"employed":[194],"reconstruct":[196],"fingerprint.":[202],"Experimental":[203],"results":[204],"comparisons":[206],"NIST":[208],"SD27":[209],"database":[212],"presented":[214],"show":[216],"effectiveness":[218],"algorithm.":[222]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}