{"id":"W2066964254","doi":"10.1142/s0218001408006302","title":"MULTIMODAL BIOMETRICS BY FACE AND HAND IMAGES TAKEN BY A CELL PHONE CAMERA","year":2008,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Royal Society of Canada","keywords":"Artificial intelligence; Computer science; Biometrics; Hand geometry; Computer vision; Face (sociological concept); Support vector machine; Feature (linguistics); Gabor filter; Pattern recognition (psychology); Facial recognition system; Feature extraction; Feature vector; Key (lock); Phone","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":[0.0002768803,0.000473311,0.0007485168,0.001351053,0.0002338157,0.000542134,0.0002670812,0.0006255076,0.006216312],"category_scores_gemma":[0.0008762834,0.0001832453,0.0003733163,0.0008719039,0.0002115531,0.0007940544,0.0004123226,0.0002906983,0.002691869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000256364,"about_ca_system_score_gemma":0.0001403496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001236623,"about_ca_topic_score_gemma":0.00190906,"domain_scores_codex":[0.999376,0.00009349392,0.00002366312,0.0001449879,0.0003181333,0.00004372183],"domain_scores_gemma":[0.9996289,0.00005648775,0.00007712739,0.0000854612,0.0001266185,0.00002535813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008376178,0.0001375892,0.01099788,0.0004594109,0.0001594298,0.0005772994,0.0002115619,0.002693723,0.3585244,0.001856755,0.006833319,0.6167111],"study_design_scores_gemma":[0.00008054029,0.002073375,0.3036835,0.0002984028,0.0004967945,0.02115872,0.0007147635,0.1395372,0.4561607,0.005153497,0.07019272,0.0004497994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4257511,0.004567976,0.5294415,0.0004768519,0.0003659031,0.0004014419,0.006669612,0.005470171,0.02685543],"genre_scores_gemma":[0.7779504,0.001913664,0.1997438,0.0002537098,0.0001809827,0.0002109216,0.00253795,0.0001532057,0.01705532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006216312,"threshold_uncertainty_score":0.02079564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06142910906458087,"score_gpt":0.287667033948913,"score_spread":0.2262379248843321,"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."}}