{"id":"W1973406954","doi":"10.1109/fg.2013.6553769","title":"Using color texture sparsity for facial expression recognition","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Local binary patterns; Pattern recognition (psychology); Computer science; Face (sociological concept); Feature (linguistics); Facial recognition system; Sparse approximation; Computer vision; Texture (cosmology); Pixel; Feature extraction; Representation (politics); Image (mathematics); Histogram","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":[0.0005411217,0.0003928519,0.0004974661,0.0008480768,0.0001845402,0.0004412702,0.000390336,0.0002586968,0.001195119],"category_scores_gemma":[0.00211222,0.0001433442,0.0003825921,0.0006256346,0.0003153593,0.0009036086,0.000505385,0.0004398607,0.0004637232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002037892,"about_ca_system_score_gemma":0.0002569004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001268559,"about_ca_topic_score_gemma":0.001611957,"domain_scores_codex":[0.9996275,0.00009098039,0.00001550575,0.00005853612,0.0001702346,0.00003723604],"domain_scores_gemma":[0.9993568,0.0002593979,0.00008465599,0.0001117989,0.0001601503,0.00002713466],"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":[0.0003899751,0.0001609338,0.003626238,0.0001469356,0.0000818195,0.0001786118,0.00009011379,0.05400464,0.1518268,0.005459677,0.003836762,0.7801974],"study_design_scores_gemma":[0.00001924536,0.0001518277,0.004669853,0.00001972511,0.00005020185,0.0005377753,0.00005290956,0.9271885,0.05945334,0.004339053,0.003473894,0.00004374075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09022648,0.0004362756,0.9046574,0.0002663986,0.00007460358,0.00005523828,0.0002528862,0.0007309036,0.003299928],"genre_scores_gemma":[0.7005723,0.0009571385,0.294678,0.0001906471,0.0001839858,0.00007133181,0.0008000716,0.0001020082,0.002444566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001268559,"threshold_uncertainty_score":0.003998101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08150713348545141,"score_gpt":0.2811164700004425,"score_spread":0.1996093365149911,"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."}}