{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001717944,0.00009384631,0.0001351037,0.0003857028,0.0001995626,0.0003091592,0.0001815137,0.00006198475,0.00001527551],"category_scores_gemma":[0.00135275,0.00009243192,0.00006736235,0.001048075,0.00003127495,0.003099115,0.00003452422,0.0002742985,0.0002536099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004310747,"about_ca_system_score_gemma":0.00005039184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001193966,"about_ca_topic_score_gemma":0.000004230159,"domain_scores_codex":[0.9985121,0.0002631093,0.0005108093,0.0001911658,0.0003833103,0.0001394591],"domain_scores_gemma":[0.9977745,0.0006269971,0.0006813029,0.0001118982,0.0007138525,0.00009143137],"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":[0.00001772567,0.00005273712,0.000003022737,0.000005204789,0.00001638161,0.000008535683,0.0008740578,0.0589399,0.001541404,0.0002957823,0.00002323821,0.938222],"study_design_scores_gemma":[0.00003581274,0.0001341148,0.0000136313,0.00002141753,0.00004159545,0.00003457313,0.003859921,0.9734477,0.01470333,0.006466673,0.001136329,0.0001048376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001059863,0.00005365386,0.9920045,0.006421658,0.0001702798,0.00009135856,4.930378e-7,0.00009649759,0.0001016656],"genre_scores_gemma":[0.5859073,0.001351303,0.4118689,0.0005000322,0.0002784022,0.00001852068,0.00002554075,0.00001600463,0.00003399705],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9381171,"threshold_uncertainty_score":0.3769265,"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."}}