{"id":"W2132450737","doi":"10.1109/iccsa.2011.69","title":"PCA Based Geometric Modeling for Automatic Face Detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Face detection; Face (sociological concept); Pattern recognition (psychology); Feature extraction; Geometric transformation; Edge detection; Object-class detection; Principal component analysis; Facial recognition system; Pixel; Feature (linguistics); Image (mathematics); Image processing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001461682,0.00006292254,0.000064702,0.0002169707,0.00008899916,0.00004288637,0.000209174,0.00004242297,0.00006525581],"category_scores_gemma":[0.00004171412,0.00005177572,0.00004745332,0.0003753021,0.000003890607,0.0003488127,0.00003181092,0.00003469097,0.00009611875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001405674,"about_ca_system_score_gemma":0.00001665036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003624013,"about_ca_topic_score_gemma":0.000005006865,"domain_scores_codex":[0.9994364,0.00001413361,0.0001203768,0.0001801638,0.0001046028,0.0001443402],"domain_scores_gemma":[0.9996288,0.0000441539,0.00003142347,0.0001882139,0.00006158258,0.00004584308],"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":[0.00001526397,0.0001646983,0.00004067803,0.00008451805,0.0000135285,0.000001402241,0.0007456415,0.006657249,0.007736919,0.000699094,0.0007545109,0.9830865],"study_design_scores_gemma":[0.0001958973,0.00006068435,0.00004057107,0.00001016746,0.000002906141,9.013376e-7,0.00002448314,0.9276773,0.06953044,0.002325971,0.00005326826,0.00007736657],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04718953,0.00001027493,0.9507623,0.00005096173,0.0001801477,0.0001693724,5.452189e-7,0.000292233,0.001344614],"genre_scores_gemma":[0.7953531,8.96499e-7,0.2043297,0.0001682619,0.000009460558,0.00004257899,9.553002e-7,0.000003713961,0.00009138551],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9830091,"threshold_uncertainty_score":0.2111353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06905308257624158,"score_gpt":0.2470178802095648,"score_spread":0.1779647976333232,"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."}}