{"id":"W2237028625","doi":"10.1007/978-1-4614-3501-3_7","title":"Face Orientation Detection Using Histogram of Optimized Local Binary Pattern","year":2012,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Histogram; Orientation (vector space); Artificial intelligence; Local binary patterns; Face (sociological concept); Pattern recognition (psychology); Face detection; Pixel; Computer science; Binary number; Computer vision; Set (abstract data type); Facial recognition system; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001709941,0.0003356244,0.0005241765,0.001085552,0.0001409214,0.0005960549,0.0007305664,0.0003493826,0.007256925],"category_scores_gemma":[0.0003083451,0.0002652642,0.0003664154,0.0009221024,0.0001529944,0.0006167852,0.0003948603,0.000330814,0.004108201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002300086,"about_ca_system_score_gemma":0.0003064034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001256325,"about_ca_topic_score_gemma":0.00211099,"domain_scores_codex":[0.999843,0.00001101807,0.000005767569,0.00003261778,0.00008708201,0.00002052195],"domain_scores_gemma":[0.9999191,0.0000164558,0.000007350028,0.00001302052,0.00003763618,0.000006351147],"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":[0.0000667425,0.00003592859,0.0005474819,0.0001114508,0.00002574444,0.00003513097,0.00001755397,0.002864912,0.07168261,0.002895703,0.009193623,0.912523],"study_design_scores_gemma":[0.00004727591,0.0002554985,0.01910621,0.0001154254,0.0001651115,0.002073445,0.0001156724,0.540252,0.344792,0.01406831,0.07884997,0.0001589772],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01356708,0.001664062,0.971369,0.0001088893,0.0001688647,0.00007997046,0.0003638198,0.003209717,0.009468554],"genre_scores_gemma":[0.1420657,0.002514362,0.8233308,0.0002159041,0.0001090553,0.0001215957,0.001533124,0.0004833889,0.02962601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007256925,"threshold_uncertainty_score":0.02427685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03136094119233775,"score_gpt":0.2509720307030729,"score_spread":0.2196110895107352,"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."}}