{"id":"W2079467126","doi":"10.1109/crv.2010.20","title":"Multispectral Face Recognition in Texture Space","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Multispectral image; Face (sociological concept); Artificial intelligence; Computer science; Computer vision; Texture (cosmology); Facial recognition system; Image texture; Space (punctuation); Pattern recognition (psychology); Image processing; Image (mathematics); Linguistics","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":[0.0001122439,0.00007650243,0.00006802012,0.0000888888,0.00003835556,0.00007798604,0.0002439328,0.00009250715,0.000271798],"category_scores_gemma":[0.000036188,0.00006384419,0.00002770981,0.0002122827,0.00001416755,0.0005077035,0.00005901777,0.0002746511,0.0007068701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007551376,"about_ca_system_score_gemma":0.00001662816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008230427,"about_ca_topic_score_gemma":0.0005035629,"domain_scores_codex":[0.9993475,0.0000207903,0.0001016862,0.000234631,0.0001150665,0.0001802997],"domain_scores_gemma":[0.9996389,0.00003542972,0.00002608386,0.0002115566,0.00002916498,0.00005893851],"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":[0.00001387016,0.0002444726,0.00233414,0.00001379931,0.000004915195,0.00004174673,0.001415055,0.00002398607,0.3691562,0.01056972,0.01496378,0.6012183],"study_design_scores_gemma":[0.003054393,0.0001875087,0.05602843,0.0001373944,0.000006216843,0.0001278569,0.0005992649,0.1738897,0.6755244,0.05544056,0.03372595,0.001278307],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7557276,0.00001482143,0.2028033,0.005229285,0.0009217734,0.0002398492,0.000003236018,0.0003312006,0.03472889],"genre_scores_gemma":[0.9307696,0.000007574175,0.0677727,0.0004084634,0.0000477452,0.0000123851,0.000006178239,0.000004174708,0.0009711575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5999399,"threshold_uncertainty_score":0.908562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01095158076249989,"score_gpt":0.2349667149283285,"score_spread":0.2240151341658286,"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."}}