{"id":"W2069705645","doi":"10.1117/12.851424","title":"Automated person categorization for video surveillance using soft biometrics","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Face recognition and analysis","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Small Business Innovation Research","keywords":"Biometrics; Computer science; Artificial intelligence; Categorization; Computer vision; Feature extraction; Feature (linguistics); Facial recognition system; Categorical variable; Frame (networking); Face (sociological concept); Machine learning","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":[0.0004586593,0.0003638994,0.0005988762,0.001289546,0.0003357183,0.0005351533,0.0006340569,0.0004620679,0.002059253],"category_scores_gemma":[0.0009409653,0.0001641577,0.0003838028,0.0004878188,0.0001628368,0.0007838244,0.0004585202,0.0003254411,0.00150689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004176389,"about_ca_system_score_gemma":0.0003074381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002451721,"about_ca_topic_score_gemma":0.003400068,"domain_scores_codex":[0.9995618,0.00007174192,0.00002256147,0.0001398017,0.0001640976,0.00004002819],"domain_scores_gemma":[0.999526,0.00008801726,0.00007916572,0.00009525489,0.000171892,0.00003962598],"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":[0.0003277934,0.0001941661,0.005377472,0.00009445411,0.00004222839,0.0001106633,0.0001194493,0.0042262,0.181768,0.001007537,0.003696212,0.8030358],"study_design_scores_gemma":[0.00006134177,0.0005977557,0.03321213,0.00005735065,0.00009460052,0.001170816,0.000234263,0.6805703,0.2681504,0.003624827,0.01212434,0.0001019008],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1154939,0.0003897701,0.8737926,0.0001360144,0.00009813291,0.0001689672,0.0004239467,0.006932515,0.002564044],"genre_scores_gemma":[0.4308836,0.0002833725,0.5639127,0.0001446454,0.00007938046,0.0001639237,0.0008781099,0.0001225894,0.003531693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002451721,"threshold_uncertainty_score":0.006888866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01705325429510164,"score_gpt":0.2452410853839016,"score_spread":0.2281878310887999,"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."}}