{"id":"W1603019584","doi":"10.1109/ivs.2006.1689598","title":"Visual Modules for Head Gesture Analysis in Intelligent Vehicle Systems","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of California, San Diego; Ryerson University","keywords":"Computer vision; Artificial intelligence; Computer science; Face (sociological concept); Eye tracking; Face detection; Invariant (physics); Facial motion capture; Gaze; Gesture; Pose; Tracking (education); Facial recognition system; Pattern recognition (psychology); Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001386683,0.00008084327,0.0001581749,0.0002191082,0.0000458303,0.000115543,0.0002590682,0.00002569112,0.000004243056],"category_scores_gemma":[0.000009935342,0.00006533134,0.00007735639,0.0007242808,0.00001099703,0.0002645747,0.00007014925,0.00004953577,0.00001158424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003905012,"about_ca_system_score_gemma":0.000008807719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003426069,"about_ca_topic_score_gemma":0.0002190249,"domain_scores_codex":[0.999176,0.00002138133,0.0002100287,0.0002691062,0.0001272401,0.0001962715],"domain_scores_gemma":[0.9996073,0.00005824355,0.00003858746,0.0002122871,0.00004856645,0.00003496546],"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":[0.00002322002,0.0005986983,0.05172512,0.00006519238,0.0001178609,0.00002228892,0.0004260108,0.3002585,0.005283011,0.2322679,0.003127259,0.4060848],"study_design_scores_gemma":[0.0001373744,0.00002192498,0.01108792,0.000008047766,0.000006568966,8.093925e-7,0.00005089193,0.9828712,0.001148219,0.001207497,0.0033657,0.00009379712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02061377,0.0002721648,0.9778344,0.0003427743,0.0001086009,0.0001355726,7.922668e-7,0.00008724909,0.0006046982],"genre_scores_gemma":[0.9234148,0.000004622462,0.07561482,0.0001393296,0.0000391853,0.00001535227,0.000003384003,0.00000414125,0.0007643429],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.902801,"threshold_uncertainty_score":0.2664135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01586629583858299,"score_gpt":0.3153521277244966,"score_spread":0.2994858318859135,"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."}}