{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007001733,0.00009436554,0.0002082738,0.0001436695,0.00009278717,0.0001797372,0.0001992094,0.00005872442,0.000001303138],"category_scores_gemma":[0.00001784062,0.00007946349,0.00008272484,0.0001047648,0.00001377243,0.0008968589,0.00002481224,0.00008098837,0.000001571081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002398738,"about_ca_system_score_gemma":0.00004815297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003189304,"about_ca_topic_score_gemma":5.301318e-7,"domain_scores_codex":[0.9990176,0.0001041249,0.0004242831,0.0001498025,0.000172732,0.0001314421],"domain_scores_gemma":[0.9988483,0.00008524405,0.0005527197,0.0001097353,0.0003129389,0.00009110627],"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":[0.00006457628,0.0002017947,0.0001009541,0.0004716874,0.0001852603,0.00006950522,0.007551854,0.0004560612,0.07035362,0.004103189,0.005073685,0.9113678],"study_design_scores_gemma":[0.003185391,0.001403528,0.001031316,0.001954877,0.0001346978,0.00422537,0.001826246,0.9531977,0.01562773,0.01357391,0.003145287,0.0006939946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.024031,0.0001785424,0.9743626,0.00005867255,0.0008810329,0.0001755598,0.000002830756,0.00001795443,0.0002918399],"genre_scores_gemma":[0.4410077,0.00002951767,0.558567,0.0001144295,0.0002455883,0.000005411529,0.000001584539,0.000009429181,0.00001931031],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9527416,"threshold_uncertainty_score":0.3240427,"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."}}