{"id":"W2119586505","doi":"10.1109/tsmcb.2004.825930","title":"Facial Expression Recognition Using Constructive Feedforward Neural Networks","year":2004,"lang":"en","type":"letter","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":246,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Instituto de Telecomunicações","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Facial expression; Sadness; Artificial neural network; Feedforward neural network; Facial recognition system; Feature (linguistics); Feed forward; Speech recognition; Anger; Psychology","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","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.000264425,0.001033215,0.000979853,0.0005501967,0.0006436517,0.0009337007,0.0007550813,0.001744791,0.0000833409],"category_scores_gemma":[0.000007435074,0.001024785,0.000375151,0.0004301189,0.0003497295,0.0005090039,0.000031417,0.002591177,0.0001520686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002703073,"about_ca_system_score_gemma":0.0001412472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002926548,"about_ca_topic_score_gemma":0.00002268221,"domain_scores_codex":[0.9947045,0.0005043852,0.001135936,0.001570754,0.001043298,0.001041123],"domain_scores_gemma":[0.9974216,0.0002415486,0.0006747112,0.0009749704,0.0003459181,0.000341271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004782721,0.001154468,0.00005154054,0.00229216,0.001362805,0.001775014,0.004478889,0.3715012,0.0026584,0.0003179527,0.3488924,0.2650369],"study_design_scores_gemma":[0.006575167,0.001616244,0.00003765446,0.008575862,0.001146968,0.002144029,0.0005958626,0.7514342,0.01368089,0.002501467,0.20511,0.00658165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01052219,0.0006668466,0.9644531,0.008105487,0.01182648,0.001992798,0.0004575387,0.000611337,0.00136425],"genre_scores_gemma":[0.8750083,0.002607351,0.01291529,0.08932315,0.009923559,0.000908396,0.000779453,0.0006998728,0.007834558],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9515378,"threshold_uncertainty_score":0.9997099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03180820635967933,"score_gpt":0.2369692993113068,"score_spread":0.2051610929516274,"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."}}