{"id":"W2004447583","doi":"10.1109/ccece.2013.6567723","title":"Automatic face recognition from video sequences using a template based cross correlation method","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Facial recognition system; Discriminant; Face (sociological concept); Three-dimensional face recognition; 3D single-object recognition; Computer vision; Cognitive neuroscience of visual object recognition; Feature (linguistics); Linear discriminant analysis; Feature extraction; Feature selection; Face detection","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":[0.0007013913,0.0004322969,0.0005476606,0.001416064,0.0002522132,0.0003941912,0.000445444,0.0004624772,0.001630223],"category_scores_gemma":[0.001498651,0.0002062478,0.0004608252,0.0009659322,0.0002527302,0.0005442428,0.0002550628,0.0004555836,0.0009727894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002313668,"about_ca_system_score_gemma":0.0004886667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001880902,"about_ca_topic_score_gemma":0.001794885,"domain_scores_codex":[0.9993217,0.0001405786,0.00004162504,0.0001751173,0.0002769251,0.00004412433],"domain_scores_gemma":[0.9994631,0.0001395235,0.00005900921,0.00008158009,0.0002344436,0.00002226979],"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":[0.0002962776,0.0001714455,0.001875691,0.0001392904,0.00008119029,0.0001856217,0.00007860899,0.01188049,0.2187532,0.002375642,0.001845001,0.7623175],"study_design_scores_gemma":[0.00004113903,0.0006887689,0.01525267,0.00004222827,0.000110138,0.002078189,0.00007007342,0.7305911,0.2418555,0.001492272,0.007679183,0.00009870727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04443517,0.0005732755,0.9519196,0.00006182891,0.000101469,0.0001254929,0.0001133853,0.0009874824,0.001682244],"genre_scores_gemma":[0.3276654,0.0009208931,0.6672774,0.0001227668,0.0001297728,0.0002344808,0.0006023084,0.00009344869,0.002953534],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001880902,"threshold_uncertainty_score":0.005453646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05343347275698308,"score_gpt":0.3187981268172489,"score_spread":0.2653646540602658,"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."}}