{"id":"W1543918438","doi":"10.1007/978-3-540-71457-6_51","title":"Classification of Facial Expressions Using K-Nearest Neighbor Classifier","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Facial expression; Computer science; Computer vision; Feature vector; k-nearest neighbors algorithm; Feature (linguistics); Face (sociological concept); Support vector machine; Point (geometry); Mathematics","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":[0.0005195757,0.0004507113,0.001098271,0.0009555352,0.0004123798,0.0005572261,0.0005674379,0.0004954739,0.002771854],"category_scores_gemma":[0.0007214614,0.0001714056,0.0006638416,0.0008513648,0.0002137125,0.0005765466,0.0002949838,0.0004244537,0.002415992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002843919,"about_ca_system_score_gemma":0.0004057232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003400005,"about_ca_topic_score_gemma":0.003680498,"domain_scores_codex":[0.9993837,0.00006255366,0.00003882497,0.0001497939,0.0002794953,0.00008562164],"domain_scores_gemma":[0.9996452,0.00006650795,0.00001863689,0.0000308159,0.0002218656,0.00001699606],"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":[0.0003360597,0.0001627793,0.003017117,0.0001239603,0.00005271402,0.00009355228,0.00007800441,0.004290975,0.06439696,0.0005542506,0.003383625,0.9235099],"study_design_scores_gemma":[0.00004127779,0.0004998532,0.03386795,0.00009740983,0.0001832417,0.001153175,0.0004495131,0.8677157,0.08767615,0.00147257,0.006746581,0.00009655698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2274804,0.002018369,0.7530136,0.0001891698,0.0005318924,0.0004421006,0.0009837144,0.003389921,0.01195087],"genre_scores_gemma":[0.6676352,0.001554872,0.3131777,0.0001035166,0.000113988,0.0002894485,0.00176124,0.0001560387,0.01520803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003400005,"threshold_uncertainty_score":0.009272754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07209120055610711,"score_gpt":0.3039888890899529,"score_spread":0.2318976885338457,"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."}}