{"id":"W2559026119","doi":"10.1109/cec.2016.7744331","title":"Multispectral hand recognition using the Kinect v2 sensor","year":2016,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"RGB color model; Principal component analysis; Multispectral image; Artificial intelligence; Pattern recognition (psychology); Support vector machine; Computer science; Biometrics; Near-infrared spectroscopy; Computer vision","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003694688,0.0007832641,0.0007375867,0.00156167,0.000244815,0.0005843908,0.0005370966,0.0005591937,0.007456969],"category_scores_gemma":[0.0005626751,0.000346955,0.0004070066,0.001151358,0.0001472563,0.0006977082,0.0006384961,0.0003058769,0.003558324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002679752,"about_ca_system_score_gemma":0.0003278446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002681725,"about_ca_topic_score_gemma":0.006174947,"domain_scores_codex":[0.9992421,0.00004771571,0.00004461383,0.0001793064,0.000445899,0.00004042534],"domain_scores_gemma":[0.9997441,0.00003067827,0.00005205959,0.00003430605,0.0001202614,0.00001851769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006630062,0.0001870878,0.005906616,0.0007942253,0.00009544141,0.0002900177,0.0001348239,0.005476388,0.5574775,0.0008509652,0.006369926,0.421754],"study_design_scores_gemma":[0.0001075633,0.0008309224,0.1658438,0.0002628604,0.0001279587,0.003130417,0.0002351614,0.2309182,0.5667197,0.001802294,0.0297105,0.0003106746],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2716306,0.001995697,0.6669305,0.0001980592,0.0002995772,0.001073549,0.01108613,0.01532527,0.03146048],"genre_scores_gemma":[0.5272666,0.00126696,0.4353365,0.0002670629,0.0000436189,0.0006325138,0.004271596,0.0003558603,0.03055933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007456969,"threshold_uncertainty_score":0.02494603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05469251163408952,"score_gpt":0.2682246685046671,"score_spread":0.2135321568705776,"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."}}