{"id":"W2051132546","doi":"10.1142/s0218126611007955","title":"A METHOD FOR FACE RECOGNITION USING IMAGE REGISTRATION","year":2011,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Feature (linguistics); Computer vision; Computer science; Zernike polynomials; Pattern recognition (psychology); Face (sociological concept); Transformation (genetics); Image registration; Facial recognition system; Outlier; Wavelet; Point (geometry); Image (mathematics); 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":[0.0009663251,0.001050387,0.001328251,0.002331977,0.000993822,0.00101995,0.002026441,0.001675516,0.006933042],"category_scores_gemma":[0.001427181,0.0005605684,0.001425848,0.00189587,0.0009424,0.001560394,0.00126337,0.00179578,0.008179387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00040903,"about_ca_system_score_gemma":0.0005587019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00081078,"about_ca_topic_score_gemma":0.0007303941,"domain_scores_codex":[0.9983038,0.0002547292,0.00008059321,0.0004003056,0.0008898496,0.00007078958],"domain_scores_gemma":[0.9994604,0.0001157976,0.00004745668,0.0001758697,0.0001773657,0.00002313284],"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":[0.0001323732,0.00009596062,0.0003898031,0.0003627155,0.0001004813,0.0002256818,0.000142928,0.005785991,0.07624065,0.01782174,0.01515455,0.883547],"study_design_scores_gemma":[0.0001427989,0.0006180849,0.003917213,0.0002232303,0.0002308472,0.008811578,0.0002029731,0.370787,0.2159109,0.02888295,0.3698949,0.0003774589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001061446,0.0005538835,0.9940724,0.00009334187,0.000263915,0.000114315,0.00006202381,0.001588047,0.002190754],"genre_scores_gemma":[0.02202148,0.0009689741,0.9670564,0.0001740616,0.0002763452,0.0003299082,0.0003770788,0.0002360597,0.008559603],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006933042,"threshold_uncertainty_score":0.0231933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09318712364927637,"score_gpt":0.2939316921098074,"score_spread":0.200744568460531,"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."}}