{"id":"W2591816305","doi":"10.1109/icdsp.2016.7868603","title":"Digital recognition from lip texture analysis","year":2016,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Liveness; Computer science; Artificial intelligence; Convolutional neural network; Texture (cosmology); Computer vision; Feature extraction; Pattern recognition (psychology); Deep learning; Image texture; Image (mathematics); Image processing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000169118,0.0004061737,0.0004274452,0.001442586,0.0001622118,0.0005856209,0.0003251257,0.0003318552,0.003455063],"category_scores_gemma":[0.0007277334,0.000123064,0.0003884862,0.0006908762,0.0002854676,0.0006297478,0.0007214741,0.0004071073,0.001634374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00027623,"about_ca_system_score_gemma":0.0002246956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001371917,"about_ca_topic_score_gemma":0.00170406,"domain_scores_codex":[0.9997947,0.00001562787,0.00001087095,0.00005131249,0.00008769241,0.00003982669],"domain_scores_gemma":[0.999764,0.00003234335,0.00003139638,0.00006771956,0.00008483996,0.00001975155],"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":[0.0006871354,0.00008410312,0.006174631,0.0002541665,0.00004309699,0.0005861244,0.00006031739,0.01168566,0.1836997,0.002273227,0.005631641,0.7888201],"study_design_scores_gemma":[0.00004705281,0.0003413036,0.05895726,0.00009181568,0.0001618969,0.002509418,0.0003736697,0.6103873,0.3019171,0.005007128,0.02011751,0.0000884736],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5680103,0.002428171,0.3975367,0.0005430808,0.0005451746,0.0002404812,0.002728971,0.002984285,0.02498288],"genre_scores_gemma":[0.9248945,0.001295214,0.06233608,0.0001406185,0.0001212222,0.00005911927,0.002169679,0.00009415222,0.008889543],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003455063,"threshold_uncertainty_score":0.01155835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01329406501843483,"score_gpt":0.2081184039451259,"score_spread":0.1948243389266911,"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."}}