{"id":"W1998131232","doi":"10.1109/roman.2014.6926273","title":"Multimodal biometric identification system for mobile robots combining human metrology to face recognition and speaker identification","year":2014,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Biometrics; Computer science; Identification (biology); Modality (human–computer interaction); Artificial intelligence; Computer vision; Face (sociological concept); Modalities; Facial recognition system; Pattern recognition (psychology); Robot; Speech recognition","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":[0.0005251607,0.0003605725,0.0005869376,0.0006247686,0.0003350095,0.0002957329,0.0005485362,0.0005948024,0.004824714],"category_scores_gemma":[0.0005518939,0.0001837064,0.0002452434,0.0002612282,0.0002097918,0.0005585584,0.0005117576,0.0002850004,0.002091942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002379672,"about_ca_system_score_gemma":0.0001850559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003954191,"about_ca_topic_score_gemma":0.0008339889,"domain_scores_codex":[0.9996064,0.00007131012,0.00001708568,0.0001048302,0.0001628882,0.00003749017],"domain_scores_gemma":[0.9996737,0.00004458375,0.00004945997,0.00004831101,0.0001524109,0.00003151786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006227037,0.0001942508,0.003258771,0.0001863079,0.00005263127,0.0001796564,0.00009830987,0.001302938,0.6061695,0.0005639402,0.003202817,0.3841682],"study_design_scores_gemma":[0.0001727656,0.007480228,0.06517716,0.0001713547,0.0004487228,0.007287696,0.0002833726,0.1793857,0.6921431,0.001550279,0.0456056,0.0002940235],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.286517,0.001359061,0.6966144,0.0003429938,0.0002294214,0.0003558112,0.0003273435,0.007157859,0.007096094],"genre_scores_gemma":[0.7213525,0.000303747,0.2693437,0.0002719392,0.0001341194,0.0002300565,0.0002713493,0.00006498727,0.008027506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004824714,"threshold_uncertainty_score":0.01614034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04209890750620349,"score_gpt":0.2911656486731221,"score_spread":0.2490667411669186,"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."}}