{"id":"W3129915554","doi":"10.1109/tpami.2022.3151865","title":"Deep Gait Recognition: A Survey","year":2022,"lang":"en","type":"preprint","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Mitacs","keywords":"Biometrics; Gait; Deep learning; Computer science; Artificial intelligence; Discriminative model; Representation (politics); Field (mathematics); Taxonomy (biology); Feature learning; Modality (human–computer interaction); Machine learning; Data science; Physical medicine and rehabilitation","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007448334,0.001082178,0.0009880518,0.002554969,0.0002150185,0.001152151,0.00118326,0.001009045,0.002967234],"category_scores_gemma":[0.002508987,0.0004162641,0.0007151776,0.002710562,0.0003653369,0.001840791,0.0007950084,0.000799277,0.002496373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003691759,"about_ca_system_score_gemma":0.0006561023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002156869,"about_ca_topic_score_gemma":0.002257015,"domain_scores_codex":[0.9994718,0.0000629624,0.00008818927,0.0001376284,0.0002062667,0.00003316642],"domain_scores_gemma":[0.9993584,0.0002935966,0.00005658038,0.00006380516,0.0001932796,0.00003440757],"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":[0.00006935864,0.00009240858,0.002697616,0.001432534,0.00006410418,0.00006648252,0.0000370864,0.004372973,0.00185645,0.002609043,0.01504692,0.9716551],"study_design_scores_gemma":[0.00006220722,0.001015734,0.02764571,0.004859881,0.0004665405,0.004296324,0.000540593,0.2918886,0.02019821,0.04203542,0.6066684,0.0003223495],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.03595386,0.4943463,0.4311151,0.003591489,0.001792994,0.0003544521,0.003875577,0.003356901,0.0256134],"genre_scores_gemma":[0.201528,0.6137013,0.1487097,0.002223344,0.002068909,0.000469376,0.01418574,0.0003813271,0.01673233],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002967234,"threshold_uncertainty_score":0.009926379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03772986746056348,"score_gpt":0.2664334556946217,"score_spread":0.2287035882340583,"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."}}