{"id":"W4400811103","doi":"10.1109/csci62032.2023.00197","title":"Thermal Face Image Classification Using Deep Learning Techniques","year":2023,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Artificial intelligence; Computer science; Face (sociological concept); Contextual image classification; Facial recognition system; Pattern recognition (psychology); Image (mathematics); Deep learning; Computer vision","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":[0.0003988569,0.0006495081,0.0006076826,0.001231221,0.000287693,0.0005156465,0.0006901944,0.0006821911,0.002751668],"category_scores_gemma":[0.0006318095,0.0002213773,0.0007890396,0.0005233869,0.0002492938,0.0006773027,0.0004934989,0.0006987778,0.001146591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005026385,"about_ca_system_score_gemma":0.0003045094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002525935,"about_ca_topic_score_gemma":0.003717177,"domain_scores_codex":[0.9997118,0.00003494896,0.00001007731,0.00007795227,0.0000994808,0.00006563107],"domain_scores_gemma":[0.9998263,0.00003407289,0.00002239989,0.00003004193,0.00007765103,0.000009719259],"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":[0.0002196561,0.0002014684,0.003110241,0.00008842356,0.00009150473,0.0001172365,0.00004996575,0.06248096,0.05293691,0.002515239,0.00601526,0.8721732],"study_design_scores_gemma":[0.000004615372,0.00005293866,0.002508014,0.00001163915,0.00002063903,0.0001178502,0.00002626218,0.9764975,0.01788898,0.001637919,0.001220135,0.00001337103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09985693,0.0008880388,0.8900318,0.0003374001,0.0001811299,0.0001173394,0.0003238454,0.00237609,0.005887487],"genre_scores_gemma":[0.8058399,0.0005753625,0.1820566,0.0002739026,0.0001238695,0.0001103202,0.0008051518,0.00008249095,0.01013245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002751668,"threshold_uncertainty_score":0.009205282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04416828411686801,"score_gpt":0.2989944235626212,"score_spread":0.2548261394457532,"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."}}