{"id":"W4378085613","doi":"10.1002/cav.2163","title":"RAIF: A deep learning‐based architecture for multi‐modal aesthetic biometric system","year":2023,"lang":"en","type":"article","venue":"Computer Animation and Virtual Worlds","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Biometrics; Artificial intelligence; Audio visual; Deep learning; Merge (version control); Modal; Architecture; Domain (mathematical analysis); Human–computer interaction; Speech recognition; Computer vision; Multimedia; Information retrieval","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008921543,0.0007703523,0.0005983325,0.0007642435,0.0003240581,0.0005084338,0.001572443,0.001005126,0.003735624],"category_scores_gemma":[0.0008090521,0.0003382928,0.0006455641,0.0005100924,0.000319832,0.0008702206,0.000776821,0.001004059,0.001807282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008718819,"about_ca_system_score_gemma":0.0007045121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008197092,"about_ca_topic_score_gemma":0.008455443,"domain_scores_codex":[0.9996305,0.00005507867,0.0000162109,0.0001202164,0.00010348,0.00007462207],"domain_scores_gemma":[0.9997832,0.00003327045,0.00002312549,0.00003326508,0.000108834,0.00001826535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004805029,0.0003248879,0.002436688,0.0001200619,0.0001961297,0.0001495947,0.00006953364,0.1167198,0.04622533,0.003356348,0.01348624,0.8164349],"study_design_scores_gemma":[0.000009994734,0.00009793098,0.0007819345,0.00001099598,0.00002181692,0.00008199501,0.000009888454,0.9857596,0.009872638,0.0009550363,0.002379541,0.00001877476],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03298555,0.0009530704,0.9531838,0.0003137056,0.0001449282,0.0001030893,0.0004068379,0.008109199,0.003799828],"genre_scores_gemma":[0.6578215,0.0005481545,0.3244794,0.0007171335,0.0001018076,0.0002174948,0.001405571,0.0001840246,0.0145249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008197092,"threshold_uncertainty_score":0.01629877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03941341375078661,"score_gpt":0.307352421743952,"score_spread":0.2679390079931654,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}