{"id":"W4367670946","doi":"10.32920/22734401","title":"On Video Based Face Recognition Through Adaptive Sparse Dictionary","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alcohol Countermeasure Systems (Canada); Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Sparse approximation; Facial recognition system; Computer vision; Face (sociological concept); Pattern recognition (psychology); Three-dimensional face recognition; Representation (politics); Inference; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002746214,0.0003457305,0.0003535395,0.0003433317,0.0001587528,0.0002289207,0.0007042206,0.0002898864,0.0006023751],"category_scores_gemma":[0.00009667084,0.0003234503,0.0003942421,0.0006052746,0.00004633726,0.0003049924,0.0006045495,0.000612541,0.005894892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001332085,"about_ca_system_score_gemma":0.0001799013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002187476,"about_ca_topic_score_gemma":0.00005192221,"domain_scores_codex":[0.9974827,0.0001902095,0.0003688493,0.001091182,0.0005690636,0.0002980081],"domain_scores_gemma":[0.9983015,0.0003682731,0.0002069058,0.0007953919,0.0002155991,0.0001123036],"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":[0.0002471124,0.002194262,0.0001111007,0.0004373003,0.001727309,0.0004886059,0.002079563,0.2874539,0.0002143464,0.05048111,0.3005931,0.3539723],"study_design_scores_gemma":[0.0004030194,0.0001322047,0.0002186118,0.0004133472,0.00006913528,0.000002867815,0.0001326328,0.8296632,0.001621375,0.1655501,0.001141911,0.0006515991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005964023,0.00002636858,0.9745995,0.005323225,0.001082643,0.0003186656,0.0001267321,0.001296491,0.01663],"genre_scores_gemma":[0.6085849,0.0006588598,0.3538885,0.01477899,0.0005855833,0.0008050087,0.002154289,0.000158625,0.01838519],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.620711,"threshold_uncertainty_score":0.9999217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.113004738464123,"score_gpt":0.2874728615847565,"score_spread":0.1744681231206335,"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."}}