{"id":"W3087845270","doi":"10.1145/3507902","title":"The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances","year":2022,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Face recognition and analysis","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Facial recognition system; Convolutional neural network; Deep learning; End-to-end principle; Discriminative model; Face (sociological concept); Frame (networking); Three-dimensional face recognition; Face detection; Pattern recognition (psychology); Computer vision; Feature (linguistics); Telecommunications","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":[0.001488438,0.001856614,0.001430047,0.002140295,0.0004195807,0.001737208,0.002236596,0.00143115,0.004267863],"category_scores_gemma":[0.003537599,0.0007346013,0.0008748606,0.002240954,0.0005002858,0.004824957,0.001422513,0.002310802,0.002932019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006382817,"about_ca_system_score_gemma":0.001332452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002490743,"about_ca_topic_score_gemma":0.002179697,"domain_scores_codex":[0.998942,0.0001453525,0.0001353023,0.0002652093,0.0004269464,0.00008524575],"domain_scores_gemma":[0.9986163,0.000618815,0.00007753192,0.0001100338,0.0005170833,0.00006021872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001326609,0.0001133744,0.001151657,0.002329681,0.00008323749,0.00008598709,0.00007412452,0.004709555,0.003134714,0.005536799,0.01672438,0.9659238],"study_design_scores_gemma":[0.00006568675,0.001673885,0.006483476,0.003914915,0.0008168597,0.002792208,0.0004912984,0.2085413,0.04008306,0.03278277,0.7020028,0.0003516832],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01198115,0.6748655,0.2892334,0.002093798,0.001651142,0.0002266215,0.0006434132,0.002142155,0.01716285],"genre_scores_gemma":[0.07443741,0.7573572,0.1442771,0.002361303,0.002662166,0.00032992,0.00424206,0.0004422149,0.01389065],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004267863,"threshold_uncertainty_score":0.01427746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1371008874422887,"score_gpt":0.3621031348838843,"score_spread":0.2250022474415957,"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."}}