{"id":"W2138735546","doi":"10.1109/ccece.2009.5090086","title":"Face verification with changeable templates","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Template; Orthonormal basis; Face (sociological concept); Set (abstract data type); Similarity (geometry); Feature (linguistics); Biometrics; Feature extraction; Pattern recognition (psychology); Artificial intelligence; Translation (biology); Data mining; Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001028983,0.0004211558,0.0008154888,0.0008381516,0.0003434588,0.0008910635,0.001463915,0.0008948263,0.002075891],"category_scores_gemma":[0.005161219,0.0003798197,0.0007113768,0.0008327093,0.0006744913,0.002028847,0.001148153,0.0008626144,0.001566436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003685512,"about_ca_system_score_gemma":0.0003196519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005084625,"about_ca_topic_score_gemma":0.0003576119,"domain_scores_codex":[0.9975792,0.0003447786,0.0001162672,0.0004744724,0.001389185,0.00009603583],"domain_scores_gemma":[0.9977143,0.0006103608,0.0002761953,0.001054414,0.0002905285,0.00005421504],"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":[0.0004952524,0.00007343236,0.001076855,0.0001500694,0.00007672343,0.0005348652,0.0002268898,0.02921087,0.1876655,0.02047458,0.001717581,0.7582973],"study_design_scores_gemma":[0.00006718741,0.0004316209,0.001796608,0.00004163475,0.00009018923,0.003547017,0.00008002234,0.6109906,0.3480875,0.01387826,0.02087,0.0001194043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01913548,0.0003728879,0.9782652,0.00006906717,0.00008033738,0.00005357618,0.00006032056,0.000614872,0.00134835],"genre_scores_gemma":[0.2985045,0.0004625443,0.6955052,0.0001234268,0.0001163249,0.00007252476,0.0002896534,0.0001608058,0.004764894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002075891,"threshold_uncertainty_score":0.006944537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01902853223423673,"score_gpt":0.2311053315679269,"score_spread":0.2120767993336902,"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."}}