{"id":"W4231674765","doi":"10.1109/icpr.2004.1333990","title":"Perceptual distance normalization for appearance detection","year":2004,"lang":"en","type":"article","venue":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Normalization (sociology); Object detection; Computer vision; Facial recognition system; Support vector machine; Classifier (UML); Subspace topology; Feature extraction; Face detection; Active appearance model; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002789319,0.0002523974,0.0002276515,0.0002474321,0.0002394441,0.0002492957,0.001053232,0.0001101676,0.0001098178],"category_scores_gemma":[0.0001658058,0.0002159596,0.0002329708,0.0004666328,0.0001102426,0.0008765847,0.00008842388,0.0001934809,0.0001073218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002018188,"about_ca_system_score_gemma":0.00008252677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004674232,"about_ca_topic_score_gemma":0.000055848,"domain_scores_codex":[0.9980251,0.00001449529,0.0005024299,0.0005271317,0.0006377434,0.0002931594],"domain_scores_gemma":[0.997574,0.00002527246,0.0004776966,0.0001757408,0.001659984,0.00008728334],"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":[0.0005245976,0.002101871,0.009042474,0.0007001248,0.0007448009,0.000003683968,0.004271854,0.002309292,0.05598001,0.09088657,0.00375223,0.8296825],"study_design_scores_gemma":[0.009487586,0.001113625,0.0142231,0.004052029,0.0002583354,0.0001402833,0.001864706,0.1187534,0.6134146,0.2301192,0.004045716,0.002527487],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0753261,0.0000545595,0.910858,0.005277799,0.001060432,0.000678941,0.0001722142,0.0001849038,0.00638707],"genre_scores_gemma":[0.996097,0.00006212214,0.002268221,0.0008143115,0.0001712216,0.0001251624,0.00003463813,0.00002044599,0.0004068329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9207709,"threshold_uncertainty_score":0.8806579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03429984372875214,"score_gpt":0.2602490524630801,"score_spread":0.225949208734328,"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."}}