{"id":"W2950449113","doi":"10.3233/jifs-17283","title":"Hyperspectral face recognition with minimum noise fraction, histogram of oriented gradient features and collaborative representation-based classifier","year":2019,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Hyperspectral imaging; Pattern recognition (psychology); Artificial intelligence; Classifier (UML); Computer science; Histogram; Facial recognition system; Representation (politics); 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":[0.0007993802,0.0006013133,0.00111566,0.001163429,0.0004615878,0.0006902694,0.0008726032,0.0008518849,0.0009844191],"category_scores_gemma":[0.00187546,0.0002933108,0.0008758452,0.0008571821,0.0004374382,0.001478721,0.0008823806,0.0007127961,0.0005729792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004345203,"about_ca_system_score_gemma":0.0005596618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00338744,"about_ca_topic_score_gemma":0.002864681,"domain_scores_codex":[0.9988048,0.000148366,0.00005637431,0.0002890914,0.0006114013,0.00009000827],"domain_scores_gemma":[0.9993703,0.0001274285,0.00009349784,0.0001075185,0.0002652186,0.00003610246],"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":[0.0001948003,0.0002927795,0.002131439,0.00008609082,0.000114635,0.00008358121,0.0000803631,0.03803423,0.06275798,0.002666343,0.003423505,0.8901343],"study_design_scores_gemma":[0.00001444882,0.0001263553,0.003423374,0.0000109257,0.00004374019,0.0002542711,0.00003233601,0.9573821,0.03505579,0.00172376,0.001894487,0.0000383851],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04635122,0.0006344091,0.9500536,0.0001961598,0.00009014642,0.00009024319,0.00008102589,0.0009271799,0.001576001],"genre_scores_gemma":[0.4657193,0.0006443126,0.5283648,0.0002654167,0.0001523457,0.0002124554,0.0004936664,0.0001096336,0.004038005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00338744,"threshold_uncertainty_score":0.006735444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01630612080220444,"score_gpt":0.2528771434521848,"score_spread":0.2365710226499804,"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."}}