{"id":"W4386802532","doi":"10.23977/jaip.2023.060510","title":"Deep learning based face recognition algorithm optimisation and application exploration","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Deep learning; Computer science; Artificial intelligence; Biometrics; Facial recognition system; Face (sociological concept); Machine learning; Identification (biology); Identity (music); Focus (optics); Authentication (law); Face Recognition Grand Challenge; Algorithm; Pattern recognition (psychology); Face detection; Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001894284,0.0007130012,0.0006673711,0.0005780518,0.0002343951,0.001215288,0.001002885,0.001221635,0.00273679],"category_scores_gemma":[0.004918884,0.0003702145,0.0005879332,0.0005160285,0.0006371411,0.00102435,0.00153367,0.001485877,0.0006410775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007338449,"about_ca_system_score_gemma":0.001454205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001850532,"about_ca_topic_score_gemma":0.002059311,"domain_scores_codex":[0.9994379,0.0002101095,0.00002739909,0.00009077967,0.0001582463,0.00007560846],"domain_scores_gemma":[0.9990345,0.0006268797,0.00003817469,0.00007431042,0.0001926119,0.00003353787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001027975,0.00009788411,0.0009451461,0.0003094749,0.00005081572,0.0001123954,0.000120933,0.7329603,0.005115301,0.03565213,0.003223034,0.2213098],"study_design_scores_gemma":[0.000005830353,0.00002822661,0.00008038919,0.00002242411,0.000005886665,0.00002460769,0.00001688121,0.9854058,0.001168516,0.01137874,0.00185811,0.000004555883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02580947,0.002878521,0.9600739,0.001625695,0.00006457338,0.00006245699,0.00005889187,0.000376039,0.009050417],"genre_scores_gemma":[0.5195909,0.003829951,0.4670287,0.0005913122,0.0001368291,0.0002706383,0.0002403058,0.0002148472,0.008096586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00273679,"threshold_uncertainty_score":0.01001805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07201738481903794,"score_gpt":0.3332009413547978,"score_spread":0.2611835565357599,"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."}}