{"id":"W4281959324","doi":"10.1117/12.2619242","title":"Deep adaptive convolutional neural network for near infrared and thermal face recognition","year":2022,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Facial recognition system; Face (sociological concept); Thermal infrared; Pattern recognition (psychology); Deep learning; Computer vision; Noise (video); Segmentation; Identification (biology); Infrared; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001733024,0.0000794414,0.00009853719,0.00003781258,0.0005264959,0.00009758057,0.0001694706,0.0000203413,0.0003994211],"category_scores_gemma":[0.00001306638,0.00007755069,0.00007085378,0.0002576023,0.00003736232,0.0002543964,0.0001721284,0.00009116459,0.00001644241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002497938,"about_ca_system_score_gemma":0.00003368541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001223902,"about_ca_topic_score_gemma":0.000008700134,"domain_scores_codex":[0.9992015,0.00008457865,0.0001239258,0.0002409385,0.0001573529,0.0001917233],"domain_scores_gemma":[0.999602,0.0001167175,0.00005202742,0.00009890433,0.0000680469,0.00006227335],"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":[0.0002436112,0.0002658357,0.001153016,0.00002139973,0.0003232367,0.0000150985,0.002026365,0.2278294,0.0004195156,0.04068792,0.02303824,0.7039764],"study_design_scores_gemma":[0.0003999596,0.0001218732,0.0008259739,0.000001517102,0.00001190581,0.00001053095,0.0002187676,0.9900296,0.00002931848,0.005819781,0.002406664,0.0001240674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03856235,0.0002142022,0.9570309,0.001397217,0.0002328814,0.0002807046,0.00003650249,0.0001504329,0.002094824],"genre_scores_gemma":[0.8719243,0.000008603503,0.1232903,0.003069922,0.0001061398,0.000193439,0.0001170888,0.000009884745,0.001280313],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8337406,"threshold_uncertainty_score":0.4373381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02753348833281977,"score_gpt":0.2267398672936174,"score_spread":0.1992063789607976,"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."}}