{"id":"W2066226233","doi":"10.1109/mwscas.2011.6026440","title":"A face portion based recognition system using multidimensional PCA","year":2011,"lang":"en","type":"article","venue":"","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Facial recognition system; Principal component analysis; Computer science; Dimensionality reduction; Curvelet; Face (sociological concept); Classifier (UML); Feature extraction; Curse of dimensionality; Feature (linguistics); Extreme learning machine; Contextual image classification; Image (mathematics); Computer vision; Artificial neural network; Wavelet transform; Wavelet","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":[0.0003253406,0.0004244682,0.0009848601,0.0007238159,0.0003808672,0.0006569019,0.001048322,0.0007454082,0.005069396],"category_scores_gemma":[0.0003974208,0.0002958203,0.0004616039,0.0005077922,0.000227682,0.001065939,0.0005410003,0.0006051908,0.004478805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002189883,"about_ca_system_score_gemma":0.0002873597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001029729,"about_ca_topic_score_gemma":0.0009221464,"domain_scores_codex":[0.9996192,0.00003406681,0.00001533337,0.0001339932,0.0001665257,0.00003077016],"domain_scores_gemma":[0.9997594,0.00003113168,0.00001770347,0.00006515442,0.0001082544,0.00001830621],"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":[0.0003605055,0.0001399585,0.0009129446,0.0001133572,0.00007329782,0.0001621182,0.00006651945,0.006909625,0.2738744,0.002664716,0.006152285,0.7085703],"study_design_scores_gemma":[0.00006794841,0.0007899917,0.006849917,0.00004047113,0.0001597152,0.001680615,0.00005618383,0.6814941,0.2765192,0.002732987,0.02947445,0.0001344086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03088118,0.0005739705,0.9567691,0.0001514243,0.000239564,0.0001602471,0.0002113621,0.006589541,0.004423571],"genre_scores_gemma":[0.2765477,0.0005774183,0.7059517,0.0003016221,0.0002062323,0.0002510908,0.0007699917,0.000154053,0.01524006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005069396,"threshold_uncertainty_score":0.01695883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06686168750195244,"score_gpt":0.2494672758846663,"score_spread":0.1826055883827138,"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."}}