{"id":"W4245337266","doi":"10.24908/iqurcp.9003","title":"Facial Recognition and Tracking using the Eigenface Technique","year":2016,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Eigenface; Artificial intelligence; Computer vision; Computer science; Face (sociological concept); Pixel; Image (mathematics); Facial recognition system; Tracking (education); Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007974553,0.0004010874,0.0006743977,0.001843061,0.0005232323,0.0008877478,0.0005474981,0.0008358223,0.003987342],"category_scores_gemma":[0.001091692,0.0004035535,0.0007322263,0.001299169,0.000403347,0.00126192,0.000710342,0.0006487785,0.003307134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003407497,"about_ca_system_score_gemma":0.00045533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002442565,"about_ca_topic_score_gemma":0.002780079,"domain_scores_codex":[0.9992342,0.00008141833,0.00003638157,0.0002470695,0.0003339501,0.00006692449],"domain_scores_gemma":[0.9996005,0.00009845554,0.00003889095,0.0001009006,0.0001430337,0.00001834258],"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":[0.00008604684,0.00007618946,0.001413136,0.00006562588,0.00005305208,0.0001105856,0.0001742609,0.008703876,0.2130757,0.007190155,0.004233022,0.7648183],"study_design_scores_gemma":[0.00002820027,0.0002583321,0.01387999,0.00007103807,0.00006531408,0.002233743,0.0001704373,0.6683563,0.2590443,0.01091077,0.04484496,0.0001365723],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008617764,0.0002796232,0.9866447,0.00006906997,0.00006278315,0.00005455564,0.00007988642,0.001570868,0.002620862],"genre_scores_gemma":[0.09723256,0.0006880651,0.893392,0.00008645114,0.00004351167,0.0001237965,0.0002383373,0.0001356794,0.008059585],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003987342,"threshold_uncertainty_score":0.01333898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1727771587481657,"score_gpt":0.3748887453626119,"score_spread":0.2021115866144461,"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."}}