{"id":"W2022432366","doi":"10.1109/icassp.2010.5494933","title":"Weakly trained dual features extraction based detector for frontal face detection","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Detector; Artificial intelligence; Computer science; Face (sociological concept); Haar-like features; Robustness (evolution); Face detection; Pattern recognition (psychology); Computer vision; Feature extraction; Object-class detection; Facial recognition system; Cluster analysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001545038,0.0001181755,0.000090299,0.00009466378,0.0001882341,0.0001443581,0.0001764412,0.000145318,0.0001061208],"category_scores_gemma":[0.00007237193,0.00009893133,0.00009084602,0.0001104964,0.00001676256,0.000582492,0.00002227966,0.0001953173,0.00005099727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001701408,"about_ca_system_score_gemma":0.00003519234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004550848,"about_ca_topic_score_gemma":0.0005875673,"domain_scores_codex":[0.9991679,0.00002320451,0.0001314574,0.0003086267,0.0001667181,0.0002021015],"domain_scores_gemma":[0.9994384,0.0001171425,0.00005904536,0.0002268324,0.00007445324,0.00008409764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003879466,0.0000369196,0.000005271201,0.000005569913,0.000003460938,9.066294e-7,0.00006672085,0.00001781174,0.8533365,0.0001594935,0.001481229,0.1448473],"study_design_scores_gemma":[0.0006636622,0.0001521097,0.002439961,0.00000603304,0.000005861592,0.00002075145,0.00006787163,0.09032151,0.8972174,0.0005206735,0.008404527,0.0001796235],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1729416,0.000005166758,0.8239391,0.0005703105,0.001324809,0.0002796369,0.000006440805,0.0002954635,0.0006373955],"genre_scores_gemma":[0.9226764,5.200099e-7,0.07602282,0.0002452238,0.0001599386,0.00008344537,0.000009667889,0.000008663644,0.0007933144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7497348,"threshold_uncertainty_score":0.4034303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01036217475388315,"score_gpt":0.2509924043485418,"score_spread":0.2406302295946586,"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."}}