{"id":"W4404388632","doi":"10.18280/ts.410545","title":"Biometric Face Identification: Utilizing Soft Computing Methods for Feature-Based Recognition","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Biometrics; Computer science; Facial recognition system; Identification (biology); Artificial intelligence; Face (sociological concept); Feature (linguistics); Pattern recognition (psychology); Soft computing; Three-dimensional face recognition; Computer vision; Speech recognition; Face detection; Artificial neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001351869,0.0001717177,0.0001601117,0.0005918485,0.0002484738,0.0006567592,0.000370393,0.0000861989,0.00006852745],"category_scores_gemma":[0.00006546122,0.000160129,0.0001551185,0.001315515,0.00002537642,0.0005275741,0.000057666,0.0001323183,0.0001017692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006303352,"about_ca_system_score_gemma":0.00006705414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003567158,"about_ca_topic_score_gemma":3.669261e-7,"domain_scores_codex":[0.9983977,0.0001466304,0.0003480816,0.0005517372,0.0002625918,0.0002932554],"domain_scores_gemma":[0.9987924,0.0006801726,0.000100841,0.000196121,0.0001437323,0.0000867104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001047964,0.00006248759,0.000007412596,0.000144379,0.00002870043,0.000002837061,0.0002609275,0.0002166596,0.05537943,0.0006271956,0.004186968,0.9390725],"study_design_scores_gemma":[0.0004433371,0.0001046719,0.0002106673,0.0002049581,0.00003426612,0.000005911764,0.00006809117,0.8459374,0.1352588,0.002984929,0.01449077,0.0002562715],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002300204,0.0007103033,0.9935559,0.001360785,0.0008819235,0.0005029237,0.00003340071,0.0004882799,0.0001662967],"genre_scores_gemma":[0.5355039,0.000007542778,0.4634734,0.0004302403,0.0002002555,0.00008534735,0.0002045564,0.00001820025,0.00007654278],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9388162,"threshold_uncertainty_score":0.652987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.066541554457275,"score_gpt":0.3558757613534764,"score_spread":0.2893342068962014,"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."}}