{"id":"W4377832584","doi":"10.18280/ts.400234","title":"An Automatic Student Attendance Monitoring System Using an Integrated HAAR Cascade with CNN for Face Recognition with Mask","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face recognition and analysis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cascade; Haar-like features; Computer science; Face (sociological concept); Facial recognition system; Artificial intelligence; Haar; Attendance; Computer vision; Pattern recognition (psychology); Computer hardware; Face detection; Engineering; Wavelet; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0004626706,0.0002426938,0.0002737832,0.0002578422,0.0002948958,0.0003847623,0.0004135118,0.00004851293,0.00001499181],"category_scores_gemma":[0.000003200292,0.000185058,0.00006170602,0.0008963883,0.00003366826,0.0008883798,0.0000299185,0.0001063066,0.00003028313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001785905,"about_ca_system_score_gemma":0.00007671779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005855976,"about_ca_topic_score_gemma":0.00004098384,"domain_scores_codex":[0.9980901,0.0001326322,0.0003261567,0.0005370289,0.0005220002,0.0003920623],"domain_scores_gemma":[0.9991257,0.00005119167,0.0001720771,0.0002702024,0.0002024025,0.0001784332],"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":[0.0003553531,0.002323125,0.01491052,0.001757106,0.001891606,0.0005381129,0.02506278,0.2264644,0.09120359,0.0006125087,0.0001485108,0.6347324],"study_design_scores_gemma":[0.001108452,0.0007038421,0.003010537,0.0006215175,0.0001254504,0.00003174652,0.008027291,0.9770606,0.00892292,0.0000145392,0.00002384627,0.0003493106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5357333,0.000006401842,0.463326,0.00005499027,0.00005853544,0.0003442203,0.00001912041,0.0004454113,0.00001208064],"genre_scores_gemma":[0.9484197,0.000002487373,0.05117321,0.0000337849,0.00009752324,0.0001449509,0.00007572298,0.00002619368,0.00002645445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7505962,"threshold_uncertainty_score":0.7546445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05088841521782842,"score_gpt":0.2948230848951997,"score_spread":0.2439346696773713,"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."}}