{"id":"W2123776888","doi":"10.1109/wacv.2007.39","title":"Local Graph Matching for Face Recognition","year":2007,"lang":"en","type":"article","venue":"Proceedings","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Computer science; Facial recognition system; Classifier (UML); Feature vector; Feature extraction; Graph; Theoretical computer science","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":[0.0005324404,0.0005715289,0.0008808694,0.002185016,0.0004501169,0.0007309915,0.001359559,0.0009259779,0.005453316],"category_scores_gemma":[0.001576822,0.0002807466,0.0008854471,0.00218194,0.0006332304,0.001609313,0.000881355,0.0008470026,0.002675813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000825857,"about_ca_system_score_gemma":0.0006080319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003634341,"about_ca_topic_score_gemma":0.004647756,"domain_scores_codex":[0.9993232,0.0001897844,0.00002434217,0.0001967212,0.0002125227,0.00005339705],"domain_scores_gemma":[0.9996169,0.0001065327,0.00004281196,0.0001512202,0.00006577928,0.00001675096],"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":[0.0001285455,0.00009854008,0.0005422409,0.0001498673,0.00008843966,0.00007023549,0.00006393818,0.08936606,0.02459985,0.02817431,0.008715808,0.8480021],"study_design_scores_gemma":[0.00001544015,0.00006174258,0.0007112566,0.00001917082,0.0000250566,0.0001911929,0.00005024708,0.9151021,0.01550646,0.06047427,0.007817032,0.00002608155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005113493,0.0004601814,0.9905612,0.00011293,0.00002880383,0.00004544743,0.0001236475,0.002186633,0.001367651],"genre_scores_gemma":[0.2652707,0.001030864,0.7251599,0.0002853045,0.0001068913,0.0002018956,0.001237753,0.0004022778,0.006304571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005453316,"threshold_uncertainty_score":0.01824319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02442758109519342,"score_gpt":0.2607327016284507,"score_spread":0.2363051205332573,"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."}}