{"id":"W2060690281","doi":"10.5244/c.20.91","title":"Tied factor analysis for face recognition across large pose changes","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Pattern recognition (psychology); Facial recognition system; Artificial intelligence; Computer science; Feature vector; Identity (music); Metric (unit); Face (sociological concept); Feature extraction; Transformation (genetics); Noise (video); Feature (linguistics); Representation (politics); Computer vision; 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":[0.000160326,0.0001121648,0.0001545398,0.0001224509,0.0002003247,0.000176144,0.0002406003,0.0000799941,0.000160775],"category_scores_gemma":[0.00001647101,0.00009311447,0.0001286637,0.0005141778,0.000009836137,0.0003606695,0.00008835864,0.0000453998,0.0001501423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001724914,"about_ca_system_score_gemma":0.000008923023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008647025,"about_ca_topic_score_gemma":0.0007145677,"domain_scores_codex":[0.9989623,0.00002642284,0.0001466763,0.0003315555,0.0001592717,0.0003738032],"domain_scores_gemma":[0.9994405,0.00007119656,0.00007249142,0.0002404298,0.000123641,0.00005177827],"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":[0.0001778632,0.001790241,0.01266126,0.0001885485,0.0009283156,0.00002001383,0.005532776,0.0004784619,0.0938422,0.005600599,0.09996262,0.7788171],"study_design_scores_gemma":[0.004652577,0.0005036037,0.05972199,0.00007043337,0.0002834999,0.00000752759,0.001418213,0.2572201,0.5747097,0.01908343,0.08044938,0.001879496],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1869542,0.00002660814,0.8101151,0.0009105494,0.0001788614,0.0002125169,0.0002191625,0.0002161326,0.001166902],"genre_scores_gemma":[0.9740508,0.00001017999,0.02209102,0.0006083914,0.0001159977,0.00007697348,0.0003071387,0.000007575267,0.002731978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7880241,"threshold_uncertainty_score":0.3797098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03624816021093658,"score_gpt":0.2909468988694341,"score_spread":0.2546987386584976,"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."}}