{"id":"W2287458595","doi":"10.1109/sitis.2015.19","title":"Partial Face Recognition Based on Template Matching","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial intelligence; Face (sociological concept); Facial recognition system; Pattern recognition (psychology); Computer science; Template matching; Feature extraction; Matching (statistics); Image (mathematics); Feature (linguistics); Computer vision; Three-dimensional face recognition; Face detection; Mathematics; Statistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002359744,0.00008127719,0.00006942312,0.00006716022,0.00006386711,0.0001186549,0.0001981973,0.00004675586,0.00007963787],"category_scores_gemma":[0.00002914939,0.00006582661,0.00003188803,0.0001288744,0.000007970771,0.0004177539,0.00004625477,0.00008122222,0.001926708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001912073,"about_ca_system_score_gemma":0.00003820751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003489407,"about_ca_topic_score_gemma":0.000003053411,"domain_scores_codex":[0.9991969,0.00005842827,0.0001174976,0.0002261912,0.0002422968,0.0001586331],"domain_scores_gemma":[0.9995136,0.00005081712,0.00003809489,0.0002164029,0.00005465939,0.0001264246],"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":[0.0002668637,0.0007190873,0.0004468085,0.0000421745,0.00002476233,0.0001025912,0.003074558,0.0198824,0.01038109,0.004316036,0.1775681,0.7831755],"study_design_scores_gemma":[0.002080029,0.0004693351,0.0002486822,0.000172425,0.000007475301,0.000013568,0.0003330779,0.8006239,0.1459172,0.03315891,0.01638606,0.0005893479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06039115,0.000003504597,0.9097533,0.001956805,0.000461341,0.0001057787,0.000002613309,0.0003226445,0.02700284],"genre_scores_gemma":[0.9525036,0.000001043559,0.04466264,0.002367036,0.00006126639,0.00001727807,0.00001699599,0.000006200303,0.0003639042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8921125,"threshold_uncertainty_score":0.9988504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0707048636881823,"score_gpt":0.2753486301982899,"score_spread":0.2046437665101076,"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."}}