{"id":"W1919500120","doi":"","title":"Vesselness features and the inverse compositional AAM for robust face recognition sing thermal IR","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Artificial intelligence; Image warping; Facial expression; Computer science; Pattern recognition (psychology); Computer vision; Face (sociological concept); Active appearance model; Expression (computer science); Facial recognition system; Local binary patterns; Image (mathematics); Histogram","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":[0.0005751024,0.0004940762,0.0004463103,0.0008278306,0.0002339069,0.0006585116,0.0006870662,0.0006642194,0.001956769],"category_scores_gemma":[0.00232881,0.0003097931,0.0009819621,0.0005799754,0.0004414734,0.0007139688,0.0005748385,0.001040127,0.0009383073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002655691,"about_ca_system_score_gemma":0.0003519246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001485434,"about_ca_topic_score_gemma":0.001374897,"domain_scores_codex":[0.9996898,0.00007396388,0.00001472786,0.00006901678,0.00012234,0.00003010069],"domain_scores_gemma":[0.9996628,0.0001416021,0.00005398461,0.00006211066,0.00006407359,0.00001544679],"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":[0.0001862131,0.00008354567,0.001062109,0.0001600986,0.00008415894,0.0001695188,0.000125577,0.2048602,0.09839368,0.01430604,0.002165335,0.6784034],"study_design_scores_gemma":[0.000002906656,0.00005251924,0.0008416786,0.00001021424,0.0000144561,0.0001116692,0.0000164483,0.985001,0.008344862,0.003923305,0.001668733,0.00001219484],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01441092,0.000144961,0.9842493,0.00006980196,0.00002721233,0.00003063399,0.00005583268,0.0003799715,0.00063139],"genre_scores_gemma":[0.4306929,0.0006024574,0.5625612,0.0001411635,0.0001281466,0.0001603697,0.0005023016,0.0003243792,0.004887002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001956769,"threshold_uncertainty_score":0.006546021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1169496569331896,"score_gpt":0.3279421512288578,"score_spread":0.2109924942956683,"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."}}