{"id":"W2467311320","doi":"10.20381/ruor-6160","title":"Human Emotion Recognition from Body Language of the Head using Soft Computing Techniques","year":2012,"lang":"en","type":"dissertation","venue":"uO Research (University of Ottawa)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Gaze; Facial expression; Movement (music); Head (geology); Computer science; Eye tracking; Computer vision; Artificial intelligence; Expression (computer science); Eye movement; Communication; Human–computer interaction; Psychology; Cognitive 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002649806,0.0003983969,0.0003614268,0.0006922441,0.0001781812,0.0007433822,0.0002111746,0.0003224344,0.001368219],"category_scores_gemma":[0.00098725,0.0001438467,0.0006368774,0.0005115133,0.0003250842,0.0005272555,0.0004693325,0.0003157688,0.0003578823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001688961,"about_ca_system_score_gemma":0.0002125748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005496971,"about_ca_topic_score_gemma":0.0006163333,"domain_scores_codex":[0.9997535,0.0000594707,0.00002085523,0.0000525787,0.00008529401,0.00002818979],"domain_scores_gemma":[0.9997459,0.0001050625,0.00004576803,0.00001787931,0.00007145828,0.00001389008],"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":[0.000377153,0.0001077594,0.006171607,0.0005603386,0.0001322185,0.0003768255,0.0006836925,0.02558994,0.4590111,0.002916263,0.001599543,0.5024734],"study_design_scores_gemma":[0.00004969084,0.0005711665,0.05282253,0.0001514798,0.0002440703,0.0007250236,0.001156023,0.7995462,0.1309912,0.008217017,0.005412386,0.0001132735],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.265459,0.0009241753,0.727111,0.0003168961,0.000156442,0.0001508476,0.0001941732,0.000521103,0.005166477],"genre_scores_gemma":[0.8651958,0.0008350829,0.1296552,0.0001291998,0.00007214771,0.0001967026,0.0002090195,0.00004619123,0.003660678],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001368219,"threshold_uncertainty_score":0.00457716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07674602896116631,"score_gpt":0.3520465884852115,"score_spread":0.2753005595240452,"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."}}