{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000377256,0.0005201562,0.0003690397,0.0004123091,0.0001789126,0.0002952522,0.0007218445,0.000486931,0.002463323],"category_scores_gemma":[0.0006547684,0.0001728177,0.0004422261,0.0003842979,0.00018358,0.0005070553,0.0004305696,0.000732717,0.0007856326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005234194,"about_ca_system_score_gemma":0.0005095321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005865816,"about_ca_topic_score_gemma":0.009557838,"domain_scores_codex":[0.9997975,0.00002522391,0.000008772147,0.00005476781,0.00007250223,0.00004121545],"domain_scores_gemma":[0.9998503,0.00003702852,0.00001816436,0.00002387942,0.00006151433,0.000009201124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003422163,0.0003013069,0.003789862,0.0001353171,0.0001509971,0.0001364606,0.00004916791,0.1658825,0.06915659,0.004539677,0.01020021,0.7453156],"study_design_scores_gemma":[0.000005480837,0.00004715362,0.001633591,0.00001157716,0.00002782395,0.00007214418,0.000009697958,0.9788468,0.0155708,0.0015712,0.00219189,0.00001185202],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1121842,0.003160797,0.8721699,0.0005109588,0.0003005723,0.00007399926,0.0006077741,0.003355207,0.00763653],"genre_scores_gemma":[0.825938,0.001028395,0.1576079,0.0004068992,0.00008935552,0.00009524826,0.001201045,0.00008651867,0.01354657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005865816,"threshold_uncertainty_score":0.01166332,"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."}}