{"id":"W4319594857","doi":"10.3390/ai4010009","title":"Recent Advances in Infrared Face Analysis and Recognition with Deep Learning","year":2023,"lang":"en","type":"article","venue":"AI","topic":"Face recognition and analysis","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Facial recognition system; Computer science; Artificial intelligence; Deep learning; Face (sociological concept); Identification (biology); Pattern recognition (psychology); Field (mathematics); Infrared; Authentication (law); Computer security","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000160082,0.00006366258,0.0001236825,0.0004508149,0.00005997024,0.00008169197,0.00009193405,0.00002368067,0.00004259569],"category_scores_gemma":[0.00004097557,0.00005472211,0.00002891872,0.00355085,0.00001672168,0.0004831757,0.00004511337,0.0000966365,0.000087492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001277764,"about_ca_system_score_gemma":0.000008956509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000108634,"about_ca_topic_score_gemma":0.0003345573,"domain_scores_codex":[0.9993364,0.00006111136,0.0001050212,0.0002313389,0.0001277805,0.0001383866],"domain_scores_gemma":[0.9997097,0.00005257118,0.00004181938,0.00009914133,0.00005254003,0.00004428861],"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":[0.0000031396,0.000009880057,0.01409753,0.000004625551,0.00004663895,0.00001042925,0.0004912962,0.003306046,0.00002297528,0.00002039182,0.00001648662,0.9819705],"study_design_scores_gemma":[0.0008095578,0.0001383204,0.0645918,0.00006414254,0.0001723301,0.000006030562,0.001423803,0.8993372,0.0008467993,0.004296893,0.02786946,0.000443623],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3909439,0.002584155,0.5971969,0.003797695,0.00007560598,0.0001812846,0.000003928655,0.0005884324,0.004628057],"genre_scores_gemma":[0.9641665,0.02576906,0.008070236,0.000721854,0.00001896937,0.00003649429,0.00009889283,0.00001040712,0.001107563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9815269,"threshold_uncertainty_score":0.2231503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01243220061288184,"score_gpt":0.2521025802729264,"score_spread":0.2396703796600445,"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."}}