{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005621364,0.00049351,0.0006077613,0.001516481,0.0002714008,0.000358485,0.0006552622,0.0004241194,0.001290921],"category_scores_gemma":[0.001752038,0.0002371342,0.0004464769,0.000552031,0.0003490837,0.0006736026,0.0002746997,0.0004524306,0.0007729675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002990758,"about_ca_system_score_gemma":0.0003803841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001698664,"about_ca_topic_score_gemma":0.001792832,"domain_scores_codex":[0.999472,0.00009857478,0.0000243679,0.0001019352,0.0002585186,0.00004463061],"domain_scores_gemma":[0.9994059,0.0001520348,0.00007840251,0.00008455565,0.0002523578,0.00002677408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002745428,0.00009887107,0.003130889,0.0001112263,0.00004664001,0.000119943,0.00009768769,0.008607485,0.2411784,0.00118682,0.002137176,0.7430102],"study_design_scores_gemma":[0.00005095275,0.0003378501,0.02034856,0.0000396825,0.00007180982,0.00129393,0.0001429516,0.6591766,0.3088384,0.002061764,0.00751012,0.0001274132],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0584501,0.0002581943,0.938821,0.0000476311,0.00005454385,0.00006641079,0.00006342942,0.001399428,0.0008393062],"genre_scores_gemma":[0.3939513,0.0003481436,0.6032915,0.00003810913,0.00003852016,0.0001128231,0.0002769308,0.0001393093,0.001803459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001698664,"threshold_uncertainty_score":0.004318535,"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."}}