{"id":"W3034330414","doi":"10.1142/s1793351x20500014","title":"Discriminative Robust Head-Pose and Gaze Estimation Using Kernel-DMCCA Features Fusion","year":2020,"lang":"en","type":"article","venue":"International Journal of Semantic Computing","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Alcohol Countermeasure Systems (Canada); Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Discriminative model; Computer science; Pose; Pattern recognition (psychology); Robustness (evolution); Computer vision; Gaze; Search engine indexing; Kernel (algebra); Feature extraction; 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":[0.0006227826,0.001160791,0.00136093,0.001551892,0.0003909039,0.0006524125,0.0009287919,0.0005163643,0.002243739],"category_scores_gemma":[0.002819833,0.0003024646,0.0009137423,0.001440674,0.0003042305,0.0009750574,0.001571019,0.000891224,0.001816646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006042643,"about_ca_system_score_gemma":0.001130958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01306821,"about_ca_topic_score_gemma":0.01976823,"domain_scores_codex":[0.9991654,0.0001382151,0.00003399138,0.0002569395,0.0002760536,0.0001294183],"domain_scores_gemma":[0.9991083,0.0001162627,0.00006756694,0.0002389097,0.0004324403,0.00003651566],"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":[0.000567362,0.0001648611,0.008032092,0.0003326461,0.0003193795,0.000214134,0.0003054597,0.04327243,0.08825418,0.003845064,0.01899377,0.8356987],"study_design_scores_gemma":[0.00003189232,0.0001669192,0.0194509,0.00006722756,0.0001015917,0.0006097527,0.0001944253,0.9072006,0.05559158,0.005483956,0.01100785,0.00009332367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05715283,0.001937655,0.9274079,0.0002547954,0.0001839536,0.0001095256,0.001717678,0.008324003,0.002911655],"genre_scores_gemma":[0.6305809,0.001006435,0.3558911,0.0001854515,0.0001246324,0.0001944318,0.005390632,0.0004364119,0.00619002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01306821,"threshold_uncertainty_score":0.02598429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03979552839053952,"score_gpt":0.307149082528898,"score_spread":0.2673535541383585,"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."}}