{"id":"W4294121878","doi":"10.46298/arima.9291","title":"Comparative study of machine learning algorithms for face recognition","year":2024,"lang":"en","type":"article","venue":"Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées","topic":"Face recognition and analysis","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Artificial intelligence; Machine learning; Computer science; Support vector machine; Naive Bayes classifier; Artificial neural network; Confusion matrix; Convolutional neural network; Facial recognition system; Biometrics; Algorithm; Deep learning; Statistical classification; Identification (biology); Random forest; Feature extraction; Pattern recognition (psychology)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006377738,0.0002821447,0.0005549325,0.0003832162,0.0000861627,0.0003490673,0.0005361745,0.0003418479,0.00002137109],"category_scores_gemma":[0.0008711328,0.0002548527,0.000199104,0.0009088754,0.00003946862,0.0009768307,0.0001686423,0.001111066,0.00004084481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002246612,"about_ca_system_score_gemma":0.000172354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006858224,"about_ca_topic_score_gemma":0.00002901228,"domain_scores_codex":[0.9964525,0.001993572,0.0007371858,0.0003182655,0.0002177452,0.0002807733],"domain_scores_gemma":[0.991639,0.007486176,0.000265455,0.0002926213,0.0002195168,0.00009720407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005862306,0.0009512116,0.00005597584,0.003051918,0.0007677835,0.00002665359,0.7522888,0.003538073,0.002362757,0.03463922,0.001714842,0.2005442],"study_design_scores_gemma":[0.0005856397,0.0005963451,0.00005728096,0.000496701,0.00009345854,0.00007553757,0.01231366,0.9369388,0.009083129,0.03058141,0.008728607,0.0004494363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05544122,0.0003144328,0.9355435,0.001337871,0.00004168339,0.001007689,0.0000395881,0.0006313119,0.005642738],"genre_scores_gemma":[0.635182,0.0007680778,0.3627275,0.0001267968,0.00004645774,0.0006219184,0.00008019761,0.00002846519,0.000418539],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9334007,"threshold_uncertainty_score":0.9999903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1565104779814885,"score_gpt":0.4020337728197476,"score_spread":0.2455232948382592,"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."}}