{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001999389,0.0002718373,0.0004821887,0.0006274377,0.0006885243,0.0001531115,0.0003903681,0.0001119444,0.0002627047],"category_scores_gemma":[0.000008256618,0.0002258216,0.00059482,0.001793413,0.00006265167,0.0004030331,0.000008773323,0.0002067755,0.00005106198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002466246,"about_ca_system_score_gemma":0.00001592513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004812302,"about_ca_topic_score_gemma":0.001630399,"domain_scores_codex":[0.9980747,0.00009237376,0.0004372026,0.0006983749,0.0002750416,0.0004223333],"domain_scores_gemma":[0.9988592,0.0002292025,0.0001582945,0.0004306633,0.0001550516,0.0001676376],"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":[0.00005920698,0.000621155,0.009665097,0.00003049437,0.003163515,0.000009486968,0.003462846,0.004625754,0.0007549163,0.000008444827,0.0000308764,0.9775682],"study_design_scores_gemma":[0.0006093693,0.0004468284,0.03670828,0.00004524087,0.002422482,0.00001772273,0.0005825126,0.7146225,0.2428609,0.0004821355,0.0001626196,0.001039453],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1633689,0.00004834139,0.8352492,0.000189104,0.0001653375,0.0001782075,0.0006865496,0.00009718986,0.00001720688],"genre_scores_gemma":[0.9968042,0.0005727359,0.001807696,0.0003671148,0.00002150737,0.00007811468,0.00006676709,0.000009932401,0.0002719135],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9765288,"threshold_uncertainty_score":0.9208737,"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."}}