{"id":"W2076707216","doi":"10.1142/s0219467814500211","title":"Fingerprint Liveness Detection Using Multiple Static Features and Random Forests","year":2014,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; University of Calgary","keywords":"Liveness; Fingerprint (computing); Computer science; Artificial intelligence; Pattern recognition (psychology); Spoofing attack; Random forest; Classifier (UML); Fingerprint recognition; Biometrics; Fingerprint Verification Competition; Noise (video); Image (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005237075,0.00005781071,0.00009406218,0.000429695,0.00006744914,0.000297482,0.0002370495,0.00003219096,0.00000121345],"category_scores_gemma":[0.000326758,0.00004841502,0.00004474029,0.0001813477,0.00005563943,0.0004445655,0.00006859496,0.000119093,3.220009e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001301174,"about_ca_system_score_gemma":0.00001834444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004432172,"about_ca_topic_score_gemma":0.00004504728,"domain_scores_codex":[0.9993031,0.00006222034,0.0002061067,0.00009657234,0.0002684044,0.00006362229],"domain_scores_gemma":[0.9990374,0.00020963,0.0002035747,0.00006996672,0.0004227718,0.00005661018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004675,0.0003769611,0.0689332,0.0001195176,0.0004791683,0.0001759754,0.004341271,0.0002266964,0.03796346,0.02834397,0.0003129496,0.8582593],"study_design_scores_gemma":[0.00418972,0.0001655642,0.4832577,0.0001272913,0.00003937766,0.002108222,0.00009020849,0.4649121,0.01161555,0.0302588,0.002957828,0.0002776067],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4134751,0.0001550783,0.5856944,0.0003159971,0.0003253391,0.00002069305,0.000001030909,0.000004572006,0.00000779917],"genre_scores_gemma":[0.9883279,0.0001581765,0.01127865,0.0001504509,0.00007571807,4.259416e-7,5.158329e-7,0.000002571529,0.000005654414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8579817,"threshold_uncertainty_score":0.2868626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0135671456756219,"score_gpt":0.2714141691401842,"score_spread":0.2578470234645623,"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."}}