{"id":"W2154211011","doi":"10.1109/tpami.2008.48","title":"Tied Factor Analysis for Face Recognition across Large Pose Differences","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":196,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Engineering and Physical Sciences Research Council","keywords":"Pattern recognition (psychology); Artificial intelligence; Facial recognition system; Computer science; Feature vector; Metric (unit); Identity (music); Feature extraction; Transformation (genetics); Face (sociological concept); Feature (linguistics); Pose; Noise (video); 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":[0.0031453,0.0007867732,0.00100633,0.001112352,0.0007039027,0.0008430724,0.001149357,0.0009196418,0.002642512],"category_scores_gemma":[0.01109101,0.0004823671,0.001529886,0.001223152,0.001180005,0.001535041,0.001543428,0.001623825,0.001668893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000643593,"about_ca_system_score_gemma":0.0006589636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003049262,"about_ca_topic_score_gemma":0.003119957,"domain_scores_codex":[0.9979735,0.0007918887,0.00007604789,0.0005447316,0.000474911,0.0001389651],"domain_scores_gemma":[0.9971805,0.001567452,0.0001975125,0.0007293019,0.0002483974,0.00007681018],"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":[0.0004435329,0.0002049822,0.003954182,0.0001180811,0.0002588412,0.0001814548,0.0003055639,0.2769232,0.01548809,0.03933449,0.003765416,0.6590222],"study_design_scores_gemma":[0.00001592374,0.00005150898,0.001956647,0.000007290027,0.00001924649,0.00009190441,0.00002256267,0.9540039,0.002967899,0.0397179,0.001117403,0.00002771283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009767214,0.0001668299,0.9891295,0.00007158019,0.00002051542,0.00002597214,0.00006248066,0.0004511464,0.0003046634],"genre_scores_gemma":[0.4237808,0.0004255882,0.5706333,0.0001688262,0.0001352778,0.000265524,0.0009672371,0.0002548193,0.003368705],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0031453,"threshold_uncertainty_score":0.01663417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05356022456716487,"score_gpt":0.2980524006241523,"score_spread":0.2444921760569874,"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."}}