{"id":"W2015549092","doi":"10.1080/18756891.2011.9727894","title":"Edge Eigenface Weighted Hausdorff Distance for Face Recognition","year":2011,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario","funders":"Beijing Institute of Technology; National Natural Science Foundation of China; Yale University","keywords":"Discriminative model; Eigenface; Pattern recognition (psychology); Facial recognition system; Artificial intelligence; Hausdorff distance; Face (sociological concept); Weighting; Computer science; Enhanced Data Rates for GSM Evolution; Hausdorff space; Mathematics; Combinatorics; Medicine","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":[0.0005531589,0.0003213468,0.0005272268,0.001200839,0.0002391087,0.0006223213,0.0005393015,0.0004132885,0.001105807],"category_scores_gemma":[0.001873067,0.0001090121,0.0003830573,0.001094082,0.0003267864,0.001129611,0.0004689419,0.0005032583,0.0004683303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004530717,"about_ca_system_score_gemma":0.0003345356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000894224,"about_ca_topic_score_gemma":0.0006482744,"domain_scores_codex":[0.9993647,0.0001597338,0.00004774287,0.0001231247,0.0002701418,0.00003456683],"domain_scores_gemma":[0.9994916,0.000172562,0.00005064079,0.000105053,0.0001555931,0.00002457351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002065779,0.00007911032,0.002279108,0.0001868187,0.0001086756,0.0001616579,0.0001145978,0.04608491,0.04603506,0.0326677,0.00511067,0.8669651],"study_design_scores_gemma":[0.00001057935,0.000157445,0.006573634,0.00002543623,0.00004159507,0.0005506778,0.00007711582,0.9217594,0.03603674,0.02412435,0.01056162,0.00008133861],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04229211,0.001976547,0.9532685,0.0001250734,0.0001015802,0.00003201987,0.0001462683,0.0004359399,0.001622024],"genre_scores_gemma":[0.5160089,0.001635976,0.4786386,0.00008064799,0.0001350739,0.0001105672,0.000581168,0.00007493263,0.002734104],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001200839,"threshold_uncertainty_score":0.003699303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07079183525587678,"score_gpt":0.3061753582838025,"score_spread":0.2353835230279257,"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."}}