{"id":"W2139139435","doi":"10.1109/icassp.2012.6288355","title":"Face recognition from video: An MMV recovery approach","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Facial recognition system; Artificial intelligence; Face (sociological concept); Pattern recognition (psychology); Machine learning; Class (philosophy); Contextual image classification; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002363126,0.0001183589,0.0001093752,0.00006743117,0.00009694057,0.0001451294,0.000346689,0.0000999104,0.0003211117],"category_scores_gemma":[0.00002534492,0.00009970243,0.00005087208,0.0001664311,0.00001347972,0.003200403,0.0001086996,0.0001081607,0.001454833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002057971,"about_ca_system_score_gemma":0.0000149368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001507177,"about_ca_topic_score_gemma":0.000004443117,"domain_scores_codex":[0.9989293,0.0001004362,0.0001614982,0.00031303,0.0002033603,0.0002924321],"domain_scores_gemma":[0.9992412,0.00006229313,0.00005696903,0.0004068894,0.00004483216,0.0001878359],"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":[0.00003365666,0.000826009,0.001019605,0.00001442647,0.00003260557,0.000001842278,0.002034587,0.00007291407,0.01458018,0.00107341,0.03260304,0.9477077],"study_design_scores_gemma":[0.004405368,0.001006171,0.03965984,0.0003086375,0.0001231517,0.0001129826,0.005709279,0.2622482,0.4828073,0.1195326,0.07943262,0.004653852],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1531205,0.00008612061,0.8165913,0.0001757858,0.0005737045,0.0001270943,0.00001268145,0.0003157001,0.0289972],"genre_scores_gemma":[0.7335317,0.00003744855,0.263623,0.001462073,0.0003402455,0.00004141213,0.0002310677,0.00001279288,0.0007203323],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9430539,"threshold_uncertainty_score":0.9993227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0449096426702331,"score_gpt":0.2468133198551188,"score_spread":0.2019036771848857,"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."}}