{"id":"https://openalex.org/W4416926421","doi":"https://doi.org/10.1145/3770656","title":"From Coarse to Fine: Fast and Effortless Facial Landmark Detection via mmWave Signals","display_name":"From Coarse to Fine: Fast and Effortless Facial Landmark Detection via mmWave Signals","publication_year":2025,"publication_date":"2025-12-02","ids":{"openalex":"https://openalex.org/W4416926421","doi":"https://doi.org/10.1145/3770656"},"language":"en","primary_location":{"id":"doi:10.1145/3770656","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3770656","pdf_url":null,"source":{"id":"https://openalex.org/S4210219751","display_name":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","issn_l":"2474-9567","issn":["2474-9567"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","raw_type":"journal-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/A5039232420","display_name":"Biyun Sheng","orcid":"https://orcid.org/0000-0002-5006-3822"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Biyun Sheng","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-5006-3822","affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shuqi Sun","orcid":"https://orcid.org/0009-0002-2821-3801"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuqi Sun","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0002-2821-3801","affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075404906","display_name":"Hui Cai","orcid":"https://orcid.org/0000-0001-8438-3843"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Cai","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-8438-3843","affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058470835","display_name":"Chen Dai","orcid":"https://orcid.org/0000-0001-9035-4539"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Dai","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-9035-4539","affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100602821","display_name":"Fu Xiao","orcid":"https://orcid.org/0000-0003-1815-2793"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fu Xiao","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-1815-2793","affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I41198531"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34778675,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":"4","first_page":"1","last_page":"25"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.3693000078201294,"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"}},"topics":[{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.3693000078201294,"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.12809999287128448,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.06159999966621399,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.8756999969482422},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.6047999858856201},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.5626000165939331},{"id":"https://openalex.org/keywords/transformation-matrix","display_name":"Transformation matrix","score":0.42730000615119934},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3896999955177307},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.388700008392334},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.36480000615119934}],"concepts":[{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.8756999969482422},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7534999847412109},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7304999828338623},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7021999955177307},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.6047999858856201},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.5626000165939331},{"id":"https://openalex.org/C165443888","wikidata":"https://www.wikidata.org/wiki/Q1482183","display_name":"Transformation matrix","level":3,"score":0.42730000615119934},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3896999955177307},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.388700008392334},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.36480000615119934},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.352400004863739},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.3066999912261963},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C2780905192","wikidata":"https://www.wikidata.org/wiki/Q2341604","display_name":"Facial muscles","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3770656","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3770656","pdf_url":null,"source":{"id":"https://openalex.org/S4210219751","display_name":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","issn_l":"2474-9567","issn":["2474-9567"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5670963527","display_name":null,"funder_award_id":"62172236, 62572253","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"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":43,"referenced_works":["https://openalex.org/W1599703818","https://openalex.org/W1940113235","https://openalex.org/W1977821862","https://openalex.org/W2016210396","https://openalex.org/W2068000634","https://openalex.org/W2087681821","https://openalex.org/W2152826865","https://openalex.org/W2194775991","https://openalex.org/W2519753233","https://openalex.org/W2963091558","https://openalex.org/W2963148250","https://openalex.org/W2963163009","https://openalex.org/W3009901566","https://openalex.org/W3016939665","https://openalex.org/W3089673251","https://openalex.org/W3169120473","https://openalex.org/W3175987492","https://openalex.org/W3210921161","https://openalex.org/W4200408870","https://openalex.org/W4205642952","https://openalex.org/W4220890766","https://openalex.org/W4225380059","https://openalex.org/W4284880289","https://openalex.org/W4295682580","https://openalex.org/W4312472600","https://openalex.org/W4313135108","https://openalex.org/W4319299976","https://openalex.org/W4380928920","https://openalex.org/W4385708165","https://openalex.org/W4387212504","https://openalex.org/W4390738584","https://openalex.org/W4392019525","https://openalex.org/W4396720269","https://openalex.org/W4396919803","https://openalex.org/W4399323529","https://openalex.org/W4401024492","https://openalex.org/W4401752322","https://openalex.org/W4402703064","https://openalex.org/W4403465751","https://openalex.org/W4403780583","https://openalex.org/W4404585418","https://openalex.org/W4404587010","https://openalex.org/W4404587394"],"related_works":[],"abstract_inverted_index":{"Facial":[0],"landmarks":[1,170],"provide":[2],"essential":[3],"representations":[4],"of":[5,65,124,180,189],"facial":[6,22,46,80,100,121,150,169,172],"states":[7],"and":[8,35,70,78,86,92,97,119,142,183],"movements,":[9],"serving":[10],"as":[11],"the":[12],"foundation":[13],"for":[14,102],"numerous":[15],"face-related":[16],"tasks.":[17],"However,":[18],"traditional":[19],"optical":[20],"device-based":[21],"landmark":[23,47],"detection":[24,48,127],"(FLD)":[25],"solutions":[26],"suffer":[27],"from":[28,117,157],"limitations":[29],"in":[30,137,160],"low-light":[31],"conditions,":[32],"occlusion":[33],"sensitivity,":[34],"privacy":[36],"concerns.":[37],"In":[38],"this":[39],"paper,":[40],"we":[41],"propose":[42],"an":[43],"efficient":[44],"two-stage":[45],"system,":[49],"CF-FLD,":[50],"which":[51,138],"utilizes":[52],"millimeter-wave":[53],"(mmWave)":[54],"radar":[55],"signals":[56],"to":[57,112,147],"reconstruct":[58],"human":[59],"faces.":[60],"Specifically,":[61],"CF-FLD":[62,165],"is":[63,110],"composed":[64],"coarse-grained":[66],"affine":[67,103],"transformation":[68,73],"(CAT)":[69],"fine-grained":[71],"offset":[72,115],"(FOT).":[74],"To":[75],"characterize":[76],"large-scale":[77],"rigid":[79],"movements":[81],"caused":[82],"by":[83],"head":[84],"poses":[85],"joint":[87],"motions,":[88],"CAT":[89,107],"defines":[90],"sparse":[91],"representative":[93],"triangle":[94],"constraints":[95],"within":[96],"across":[98],"different":[99],"parts":[101],"transformation.":[104],"Based":[105],"on":[106,154],"results,":[108],"FOT":[109,130],"presented":[111],"progressively":[113],"obtain":[114],"shifts":[116],"subtle":[118],"non-rigid":[120],"deformations.":[122],"Instead":[123],"resource-intensive":[125],"area":[126],"or":[128],"search,":[129],"designs":[131],"a":[132,175,184,192],"multi-level":[133],"region":[134],"partition":[135],"strategy,":[136],"region-wise":[139],"hybrid":[140],"network":[141],"region-aware":[143],"attention":[144],"are":[145],"constructed":[146],"hierarchically":[148],"refine":[149],"landmarks.":[151],"Comprehensive":[152],"evaluations":[153],"data":[155],"collected":[156],"20":[158],"participants":[159],"real-world":[161],"environments":[162],"demonstrate":[163],"that":[164],"can":[166],"accurately":[167],"localize":[168],"during":[171],"motion,":[173],"achieving":[174],"mean":[176,186],"absolute":[177],"error":[178,187],"(MAE)":[179],"2.02":[181],"mm":[182],"normalized":[185],"(NME)":[188],"3.37%":[190],"at":[191],"low":[193],"cost.":[194]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-12-02T00:00:00"}
