{"id":"W2139353402","doi":"10.1109/icip.1997.647396","title":"MPEG4 face modeling using fiducial points","year":2002,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Fiducial marker; Computer science; Computer vision; Artificial intelligence; Face (sociological concept); Computer facial animation; Facial expression; Animation; Active appearance model; Coding (social sciences); Facial motion capture; Facial recognition system; Computer graphics (images); Image (mathematics); Computer animation; Face detection; Feature extraction; Mathematics","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":[0.00007500266,0.00006440093,0.00008158986,0.00008018018,0.00009633564,0.0001150442,0.0002437186,0.00002746192,0.0006587578],"category_scores_gemma":[0.00001446435,0.00005743926,0.00006779539,0.000284139,0.000007187736,0.0003306066,0.00007618171,0.00005179345,0.0005304621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001700881,"about_ca_system_score_gemma":0.000006277364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000342539,"about_ca_topic_score_gemma":0.000005975945,"domain_scores_codex":[0.9993593,0.00002132121,0.0001142677,0.0001996656,0.0001494642,0.0001559585],"domain_scores_gemma":[0.9996848,0.00001010737,0.00001986629,0.00018387,0.0000410092,0.0000604118],"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":[0.000004032647,0.0006003537,0.0004276056,0.00003806884,0.0002127165,0.00007533349,0.005380166,0.3458426,0.01095312,0.04269169,0.007477896,0.5862964],"study_design_scores_gemma":[0.0000835608,0.00000394889,0.000001475632,0.000004868108,0.00000420469,0.000005191501,0.00004060115,0.9983838,0.0005690369,0.0006355352,0.000179368,0.00008846498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03194841,0.00003708565,0.9577069,0.001042572,0.00008403198,0.00002412957,3.946271e-7,0.0001181436,0.009038347],"genre_scores_gemma":[0.9100866,0.00001476175,0.08802588,0.0006754478,0.0000413435,6.649982e-7,3.919301e-7,0.000003581402,0.001151361],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8781382,"threshold_uncertainty_score":0.7212935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08754407524048695,"score_gpt":0.2612533536393343,"score_spread":0.1737092783988473,"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."}}