{"id":"W7125505986","doi":"10.1109/icft66708.2025.11336599","title":"Active Attendance Monitoring via Eye and Facial Expression Recognition","year":2025,"lang":"","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hatch (Canada)","funders":"","keywords":"Facial expression recognition; Facial expression; Facial recognition system; Attendance; Eye tracking; Expression (computer science)","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":[0.0003127213,0.000530066,0.0005714976,0.0006502126,0.0001946397,0.000527664,0.0004596548,0.0005185328,0.004933772],"category_scores_gemma":[0.001238603,0.0001509504,0.0002270312,0.0004049217,0.0001100912,0.0004504961,0.0005184577,0.0005021208,0.002127754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001672346,"about_ca_system_score_gemma":0.0001976402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00100461,"about_ca_topic_score_gemma":0.002784079,"domain_scores_codex":[0.9995487,0.0000754614,0.00001821951,0.0001494841,0.0001434471,0.0000645945],"domain_scores_gemma":[0.9993615,0.0001334546,0.0001547675,0.0000650107,0.0002032682,0.00008206829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0024832,0.0004642797,0.05246578,0.0002672266,0.0001417484,0.0001741629,0.0003007001,0.0006522042,0.6582182,0.000434564,0.004868105,0.2795298],"study_design_scores_gemma":[0.0001998256,0.002145292,0.7405285,0.00007744744,0.0004382231,0.001687714,0.0004828057,0.05302747,0.1942781,0.001076743,0.00591635,0.0001415393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.881621,0.0007127711,0.09634109,0.0002492677,0.0002662546,0.0003827096,0.003174704,0.001723614,0.01552865],"genre_scores_gemma":[0.9677609,0.0002500832,0.02336256,0.000178119,0.0001599144,0.0001835051,0.0007426822,0.0001362192,0.007226079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004933772,"threshold_uncertainty_score":0.01650512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03584111273210435,"score_gpt":0.3477969559365171,"score_spread":0.3119558432044128,"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."}}