{"id":"W1985244783","doi":"10.1109/ccece.2014.6901065","title":"One-shot facial feature extraction based on Gauss-Laguerre filter","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Feature extraction; Artificial intelligence; Computer science; Pattern recognition (psychology); Facial recognition system; Filter (signal processing); Feature (linguistics); Face (sociological concept); Computer vision","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":[],"consensus_categories":[],"category_scores_codex":[0.000131076,0.0001028938,0.00009109228,0.00008853376,0.0001132939,0.0001299706,0.0002461963,0.0001103178,0.000373637],"category_scores_gemma":[0.00003183977,0.00008570553,0.00005618243,0.0001295284,0.00001171169,0.0003819666,0.00003527807,0.0001633715,0.0007028898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001537944,"about_ca_system_score_gemma":0.00001518082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001227639,"about_ca_topic_score_gemma":0.00001007581,"domain_scores_codex":[0.9990978,0.00005997501,0.00009239151,0.0003028239,0.0002683901,0.0001786209],"domain_scores_gemma":[0.999422,0.00007298764,0.00004264642,0.0003399786,0.00004474909,0.0000775949],"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":[0.00009084489,0.0004185462,0.0003580986,0.00002929611,0.00001033426,0.00000707934,0.0002032118,0.0009379283,0.05926896,0.004970512,0.1965573,0.7371479],"study_design_scores_gemma":[0.001416701,0.0004991818,0.01451997,0.0001457597,0.00001077335,0.000006485834,0.00003408275,0.5947083,0.1449575,0.002666887,0.2404053,0.0006290575],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01335964,0.000005092772,0.8556522,0.007901337,0.001031698,0.0001776584,0.000004068499,0.0003900532,0.1214783],"genre_scores_gemma":[0.966831,0.000002436445,0.02507955,0.004805361,0.0002118103,0.00001559116,0.00002276918,0.000007529914,0.003023912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9534714,"threshold_uncertainty_score":0.903446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04191894222432213,"score_gpt":0.2807830863870932,"score_spread":0.238864144162771,"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."}}