{"id":"W2123247936","doi":"10.1109/mmsp.2009.5293308","title":"Automatic fiducial points detection for facial expressions using scale invariant feature","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Fiducial marker; Computer science; Computer vision; Pattern recognition (psychology); Facial recognition system; Face detection; AdaBoost; Normalization (sociology); Feature extraction; Feature (linguistics); Face (sociological concept); Detector; Object-class detection; Facial expression; Classifier (UML)","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":[0.0001318132,0.0001114679,0.0001202736,0.00009842614,0.0003368553,0.0001481199,0.0002512758,0.0001160855,0.00005354326],"category_scores_gemma":[0.0000522371,0.00008979447,0.00007894568,0.0001902331,0.00001067821,0.0006058773,0.00005320533,0.00009235273,0.00003108344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003016365,"about_ca_system_score_gemma":0.00004074263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001091755,"about_ca_topic_score_gemma":0.00001300492,"domain_scores_codex":[0.9991322,0.00003951083,0.0001484078,0.0002807361,0.0001702811,0.0002289159],"domain_scores_gemma":[0.9995208,0.00003996454,0.00006388166,0.0002268932,0.00006442455,0.00008405417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002209254,0.0001286536,0.00001032825,0.00001544657,0.000006550789,0.000003146447,0.0006921021,0.00007533756,0.5371987,0.0006499537,0.006820707,0.454377],"study_design_scores_gemma":[0.0008338263,0.0001804716,0.001346367,0.0001232441,0.00001419004,0.00002985085,0.00007430273,0.6743595,0.3072236,0.0131466,0.002359529,0.000308478],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1830597,0.000008284299,0.8139594,0.001334209,0.0004763583,0.0003013849,0.000004508773,0.0002555168,0.0006006784],"genre_scores_gemma":[0.7358411,0.000001552991,0.2627544,0.0009447328,0.000188153,0.00001888007,0.00000544092,0.00000578245,0.0002399405],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6742842,"threshold_uncertainty_score":0.3661712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02350842265509395,"score_gpt":0.2729554198987224,"score_spread":0.2494469972436284,"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."}}