{"id":"W2078277829","doi":"10.1109/t-affc.2012.30","title":"Projection into Expression Subspaces for Face Recognition from Single Sample per Person","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Linear subspace; Subspace topology; Expression (computer science); Artificial intelligence; Projection (relational algebra); Pattern recognition (psychology); Linear discriminant analysis; Facial recognition system; Face (sociological concept); Facial expression; Computer science; Sample (material); Set (abstract data type); Biometrics; Discriminant; Image (mathematics); Computer vision; Mathematics; Algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003307531,0.0002671025,0.0002264896,0.0002430787,0.0007826855,0.0001639238,0.0002217912,0.000165649,0.00003940715],"category_scores_gemma":[0.00004687009,0.000255234,0.0001875693,0.0003032132,0.00003667413,0.001248349,0.000006135581,0.0002727478,0.0001058613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002090488,"about_ca_system_score_gemma":0.00002560405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002598728,"about_ca_topic_score_gemma":0.00003861947,"domain_scores_codex":[0.9982696,0.0002348648,0.0002104287,0.0005630956,0.000272692,0.0004493757],"domain_scores_gemma":[0.9981056,0.001149785,0.0001605452,0.0002722394,0.0001669525,0.0001449083],"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":[0.0001124291,0.0007060321,0.0001194423,0.00004644841,0.00004758394,3.118164e-7,0.0123143,0.001550926,0.3052736,0.00001045102,0.0003424815,0.679476],"study_design_scores_gemma":[0.0009506287,0.0004620853,0.0005903788,0.0003332008,0.00004175873,0.000006192919,0.002324059,0.08294087,0.9107665,0.0006396172,0.000451913,0.0004928204],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2696253,0.00004918615,0.7275946,0.0001243223,0.001552033,0.0005821505,0.00003394042,0.0003296209,0.000108881],"genre_scores_gemma":[0.8515437,0.000005951914,0.1478969,0.0001147868,0.0002478346,0.00011259,0.00002791196,0.00002538493,0.0000250073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6789832,"threshold_uncertainty_score":0.99999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05310404105780693,"score_gpt":0.2796202461226605,"score_spread":0.2265162050648536,"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."}}