{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008930457,0.00009897472,0.0001009091,0.00006144469,0.0002156544,0.0001582375,0.0002305725,0.0001056249,0.0003588408],"category_scores_gemma":[0.00003548985,0.00007785378,0.00006125047,0.0001084976,0.00001498932,0.00102582,0.0001074847,0.00006498418,0.000375962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002074208,"about_ca_system_score_gemma":0.00002299142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005600444,"about_ca_topic_score_gemma":0.000004261368,"domain_scores_codex":[0.9992124,0.00003090789,0.000137993,0.0002737507,0.0001366829,0.00020824],"domain_scores_gemma":[0.9994648,0.00005494326,0.00006289696,0.0001750839,0.000161182,0.00008116355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002451363,0.0001239204,0.0001375231,0.00003249663,0.000007914183,0.000001319751,0.0003187062,0.00004233647,0.5603955,0.0003350263,0.06842372,0.370157],"study_design_scores_gemma":[0.00124382,0.0001638679,0.0009100052,0.0001368572,0.00001232943,0.00001340455,0.0001964369,0.3073464,0.6425689,0.03429008,0.01258967,0.0005282608],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2427474,0.000008204239,0.7540895,0.0004273342,0.0003487573,0.0005129543,0.000007973075,0.0001571597,0.001700673],"genre_scores_gemma":[0.5508788,0.000005393026,0.4469614,0.00112097,0.0001775271,0.0001226695,0.00003710069,0.000009855478,0.0006862906],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3696287,"threshold_uncertainty_score":0.4832357,"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."}}