{"id":"https://openalex.org/W7131403062","doi":"https://doi.org/10.1109/icdm65498.2025.00041","title":"HadaSmileNet: Hadamard Fusion of Handcrafted and Deep-Learning Features for Enhancing Facial Emotion Recognition of Genuine Smiles","display_name":"HadaSmileNet: Hadamard Fusion of Handcrafted and Deep-Learning Features for Enhancing Facial Emotion Recognition of Genuine Smiles","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W7131403062","doi":"https://doi.org/10.1109/icdm65498.2025.00041"},"language":null,"primary_location":{"id":"doi:10.1109/icdm65498.2025.00041","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm65498.2025.00041","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining (ICDM)","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/A5100577622","display_name":"Mohammad Junayed Hasan","orcid":"https://orcid.org/0009-0008-3451-0267"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]},{"id":"https://openalex.org/I2799853436","display_name":"Johns Hopkins Medicine","ror":"https://ror.org/037zgn354","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799853436"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mohammad Junayed Hasan","raw_affiliation_strings":["Johns Hopkins University,CS Department,Baltimore,MD,USA"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins University,CS Department,Baltimore,MD,USA","institution_ids":["https://openalex.org/I145311948","https://openalex.org/I2799853436"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121954363","display_name":"Nabeel Mohammed","orcid":null},"institutions":[{"id":"https://openalex.org/I157386601","display_name":"North South University","ror":"https://ror.org/05wdbfp45","country_code":"BD","type":"education","lineage":["https://openalex.org/I157386601"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Nabeel Mohammed","raw_affiliation_strings":["North South University,Apurba NSU R&#x0026;D Lab,Dhaka,Bangladesh"],"affiliations":[{"raw_affiliation_string":"North South University,Apurba NSU R&#x0026;D Lab,Dhaka,Bangladesh","institution_ids":["https://openalex.org/I157386601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015028096","display_name":"Shafin Rahman","orcid":null},"institutions":[{"id":"https://openalex.org/I157386601","display_name":"North South University","ror":"https://ror.org/05wdbfp45","country_code":"BD","type":"education","lineage":["https://openalex.org/I157386601"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Shafin Rahman","raw_affiliation_strings":["North South University,Apurba NSU R&#x0026;D Lab,Dhaka,Bangladesh"],"affiliations":[{"raw_affiliation_string":"North South University,Apurba NSU R&#x0026;D Lab,Dhaka,Bangladesh","institution_ids":["https://openalex.org/I157386601"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126838878","display_name":"Philipp Koehn","orcid":null},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]},{"id":"https://openalex.org/I2799853436","display_name":"Johns Hopkins Medicine","ror":"https://ror.org/037zgn354","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799853436"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philipp Koehn","raw_affiliation_strings":["Johns Hopkins University,CS Department,Baltimore,MD,USA"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins University,CS Department,Baltimore,MD,USA","institution_ids":["https://openalex.org/I145311948","https://openalex.org/I2799853436"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100577622"],"corresponding_institution_ids":["https://openalex.org/I145311948","https://openalex.org/I2799853436"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.81805225,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"337","last_page":"346"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9049999713897705,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9049999713897705,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.04190000146627426,"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/T10057","display_name":"Face and Expression Recognition","score":0.011500000022351742,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7299000024795532},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5418999791145325},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5406000018119812},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5163000226020813},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49549999833106995},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4514999985694885},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.436599999666214},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.4284000098705292},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.4246000051498413},{"id":"https://openalex.org/keywords/hadamard-transform","display_name":"Hadamard transform","score":0.4174000024795532}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7775999903678894},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7299000024795532},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6646999716758728},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5418999791145325},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5406000018119812},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5163000226020813},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49549999833106995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48089998960494995},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4514999985694885},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.436599999666214},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.4284000098705292},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.4246000051498413},{"id":"https://openalex.org/C60292330","wikidata":"https://www.wikidata.org/wiki/Q1014065","display_name":"Hadamard transform","level":2,"score":0.4174000024795532},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4165000021457672},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3815999925136566},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.36230000853538513},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.33239999413490295},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C6438553","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Affective computing","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.25679999589920044},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdm65498.2025.00041","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm65498.2025.00041","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining (ICDM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1588539311","https://openalex.org/W1968579112","https://openalex.org/W1998022503","https://openalex.org/W2020825481","https://openalex.org/W2035343574","https://openalex.org/W2047504516","https://openalex.org/W2129224356","https://openalex.org/W2141208341","https://openalex.org/W2173219554","https://openalex.org/W2268421884","https://openalex.org/W2511084678","https://openalex.org/W2574726746","https://openalex.org/W2744326989","https://openalex.org/W2799041689","https://openalex.org/W2912536425","https://openalex.org/W3010796497","https://openalex.org/W3033062106","https://openalex.org/W3094502228","https://openalex.org/W3106579356","https://openalex.org/W3118496208","https://openalex.org/W3158405343","https://openalex.org/W3159819734","https://openalex.org/W3168718178","https://openalex.org/W3197864072","https://openalex.org/W4304789371","https://openalex.org/W4403157406","https://openalex.org/W4409474101"],"related_works":[],"abstract_inverted_index":{"The":[0,110,163],"distinction":[1],"between":[2],"genuine":[3],"and":[4,24,59,133,144,166],"posed":[5],"emotions":[6],"represents":[7],"a":[8,68],"fundamental":[9],"pattern":[10],"recognition":[11],"challenge":[12],"with":[13,40,78],"significant":[14],"implications":[15],"for":[16,44,117,172],"data":[17,177],"mining":[18,178],"applications":[19,179],"in":[20,35,175],"social":[21],"sciences,":[22],"healthcare,":[23],"human-computer":[25],"interaction.":[26],"While":[27],"recent":[28],"multitask":[29,149],"learning":[30,38,119],"frameworks":[31],"have":[32],"shown":[33],"promise":[34],"combining":[36],"deep":[37,118],"architectures":[39],"handcrafted":[41],"D-Marker":[42],"features":[43],"smile":[45],"facial":[46],"emotion":[47],"recognition,":[48],"these":[49],"approaches":[50],"exhibit":[51],"computational":[52,108,138],"inefficiencies":[53],"due":[54],"to":[55,148],"auxiliary":[56],"task":[57],"supervision":[58],"complex":[60],"loss":[61],"balancing":[62],"requirements.":[63],"This":[64],"paper":[65],"introduces":[66],"HadaSmileNet,":[67],"novel":[69],"feature":[70,104,152],"fusion":[71,90,97],"framework":[72],"that":[73,94,180],"directly":[74],"integrates":[75],"transformer-based":[76],"representations":[77],"physiologically-grounded":[79],"D-Markers":[80],"through":[81,158],"parameter-free":[82],"multiplicative":[83],"interactions.":[84],"Through":[85],"systematic":[86],"evaluation":[87],"of":[88],"15":[89],"strategies,":[91],"we":[92],"demonstrate":[93],"Hadamard":[95],"multi-plicative":[96],"achieves":[98],"optimal":[99],"performance":[100],"by":[101],"enabling":[102],"direct":[103,159],"interactions":[105],"while":[106,151],"maintaining":[107],"efficiency.":[109],"proposed":[111],"approach":[112],"establishes":[113],"new":[114],"state-of-the-art":[115],"results":[116],"methods":[120],"across":[121],"four":[122],"benchmark":[123],"datasets:":[124],"UvA-NEMO":[125],"(88.7%,":[126],"+0.8%),":[127],"MMI":[128],"(99.7%),":[129],"SPOS":[130],"(98.5%,":[131],"+0.7%),":[132],"BBC":[134],"(100%,":[135],"+5.0%).":[136],"Comprehensive":[137],"analysis":[139],"reveals":[140],"26%":[141],"parameter":[142],"reduction":[143],"simplified":[145],"training":[146],"compared":[147],"alternatives,":[150],"visualization":[153],"demonstrates":[154],"enhanced":[155],"discriminative":[156],"power":[157],"domain":[160],"knowledge":[161],"integration.":[162],"framework's":[164],"efficiency":[165],"effectiveness":[167],"make":[168],"it":[169],"particularly":[170],"suitable":[171],"practical":[173],"deployment":[174],"multimedia":[176],"require":[181],"realtime":[182],"affective":[183],"computing":[184],"capabilities.":[185]},"counts_by_year":[],"updated_date":"2026-02-27T06:17:20.405678","created_date":"2026-02-26T00:00:00"}
