{"id":"W4411055221","doi":"10.1109/access.2025.3576639","title":"Hierarchical Multi-Scale Patch Attention and Global Feature-Adaptive Fusion for Robust Occluded Face Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Face recognition and analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Windsor","funders":"","keywords":"Computer science; Facial recognition system; Artificial intelligence; Face (sociological concept); Pattern recognition (psychology); Scale (ratio); Feature (linguistics); Fusion; Feature extraction; Computer vision","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":[],"consensus_categories":[],"category_scores_codex":[0.0001841363,0.0001476148,0.0001934565,0.0001213094,0.0002398228,0.0003732547,0.0004147708,0.0001257659,0.000006665457],"category_scores_gemma":[0.00003596579,0.0001365481,0.0001167719,0.0006484147,0.00004787319,0.0006806627,0.0001730884,0.0001258843,0.00001398801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006077077,"about_ca_system_score_gemma":0.00004152902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008386217,"about_ca_topic_score_gemma":0.0003533286,"domain_scores_codex":[0.9988421,0.00008214962,0.0001834864,0.0004978353,0.0001655928,0.0002288785],"domain_scores_gemma":[0.9993513,0.00008238937,0.00008060727,0.0002023085,0.0001955795,0.00008784269],"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":[0.0001431062,0.0004066171,0.008732446,0.0001236391,0.0001391054,0.000007882355,0.0001899587,0.0002857927,0.003828377,0.0004976147,0.006721652,0.9789238],"study_design_scores_gemma":[0.002314287,0.00009233727,0.0359254,0.0001944662,0.0001058022,0.000008899607,0.0001283888,0.9464352,0.005548056,0.008381862,0.0004695054,0.0003957909],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08954594,0.00006935085,0.9067473,0.002396077,0.0004066904,0.0003315244,0.00005868722,0.0001138614,0.0003305434],"genre_scores_gemma":[0.8177569,0.0001364816,0.1795035,0.001294531,0.00009468363,0.0001223699,0.00009572443,0.00001005604,0.0009857942],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.978528,"threshold_uncertainty_score":0.5568269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05106652928747446,"score_gpt":0.3206489229432232,"score_spread":0.2695823936557488,"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."}}