{"id":"W4383501315","doi":"10.1109/icscss57650.2023.10169193","title":"Survey on Face Recognition using an Improved VGGNET Convolutional Neural Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Convolutional neural network; Pooling; Artificial intelligence; Facial recognition system; Convolution (computer science); Face (sociological concept); Identification (biology); Deep learning; Pattern recognition (psychology); Upgrade; Function (biology); Machine learning; Artificial neural network","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0009074986,0.0007074667,0.0005288278,0.001881359,0.0002497966,0.0006780632,0.001379085,0.0007586249,0.003515933],"category_scores_gemma":[0.001010476,0.0003702766,0.0006692816,0.001597163,0.0002995058,0.001670958,0.0004548498,0.0006994278,0.001497333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007607631,"about_ca_system_score_gemma":0.0008502064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01011,"about_ca_topic_score_gemma":0.007273502,"domain_scores_codex":[0.9993563,0.0000649279,0.00005446406,0.0001604564,0.0003097702,0.00005413415],"domain_scores_gemma":[0.9995731,0.00009956768,0.0000184837,0.00005092252,0.0002435702,0.00001441302],"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":[0.0001352473,0.00009671607,0.002316081,0.0006246172,0.0001144437,0.00006680747,0.0000314155,0.007979583,0.007160486,0.004150453,0.01208086,0.9652434],"study_design_scores_gemma":[0.00007223497,0.0009241123,0.01834393,0.0007087787,0.0005714658,0.002383175,0.000189385,0.5040921,0.1000707,0.009768859,0.3626527,0.0002226197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06738409,0.2036793,0.6559331,0.002357424,0.001473501,0.0003141103,0.0009440774,0.004170468,0.06374392],"genre_scores_gemma":[0.4708182,0.202057,0.2383778,0.002205898,0.0008406446,0.0002760544,0.005975643,0.0004465103,0.0790021],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01011,"threshold_uncertainty_score":0.02010226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1130349346010295,"score_gpt":0.2982939695627509,"score_spread":0.1852590349617215,"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."}}