{"id":"W3120590167","doi":"10.18280/ts.370606","title":"A Face Detection Method Based on Skin Color Model and Improved AdaBoost Algorithm","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face recognition and analysis","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Suzhou University","keywords":"Artificial intelligence; AdaBoost; Face detection; Pattern recognition (psychology); Computer science; Particle swarm optimization; Color space; Brightness; Facial recognition system; Face (sociological concept); Computer vision; Classifier (UML); Mathematics; Algorithm; Image (mathematics)","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":[0.0002208707,0.0001394296,0.0001595521,0.00008262992,0.0001098133,0.0001231494,0.0001875354,0.00004385424,0.00006162611],"category_scores_gemma":[0.00001234724,0.0001265665,0.00008241495,0.0002892556,0.00001660499,0.0001734918,0.00004560708,0.0001064687,0.00001983222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002833572,"about_ca_system_score_gemma":0.00003708825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001188879,"about_ca_topic_score_gemma":0.000007441601,"domain_scores_codex":[0.9989223,0.00008865036,0.0001817546,0.0003927688,0.0002382142,0.000176339],"domain_scores_gemma":[0.9995446,0.00006560452,0.00006547268,0.0001081684,0.00004981801,0.0001663903],"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":[0.00004163259,0.000118337,0.000003247813,0.00001981405,0.00004461272,0.000005475355,0.0006220379,0.08809747,0.0417556,0.0001540958,0.0001223657,0.8690153],"study_design_scores_gemma":[0.0008018904,0.0002993087,0.00003816894,0.000006800643,0.00002394095,0.000001161588,0.00005237581,0.9741056,0.02412088,0.00008428433,0.0003162298,0.0001493516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002864247,0.00001063544,0.9940444,0.002594283,0.00002285238,0.0001905037,0.00001774025,0.0001160274,0.0001393105],"genre_scores_gemma":[0.7563598,0.000003565171,0.239651,0.003859468,0.00004234773,0.00003414653,0.000005994547,0.000008076963,0.00003560668],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8860081,"threshold_uncertainty_score":0.5161234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01928909288407091,"score_gpt":0.2428570898528312,"score_spread":0.2235679969687603,"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."}}