{"id":"W2590291869","doi":"10.1109/icci-cc.2016.7862089","title":"Soft biometric: Give me your favorite images and i will tell your gender","year":2016,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Artificial intelligence; Face (sociological concept); Fingerprint (computing); Perception; Image (mathematics); Selection (genetic algorithm); Filter (signal processing); Ridge; Pattern recognition (psychology); Computer vision; Psychology","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":[0.0004282478,0.0005357004,0.0004549549,0.0007409027,0.0003042363,0.0005729988,0.0002685136,0.0005051935,0.02247245],"category_scores_gemma":[0.001713479,0.0001312946,0.0002200023,0.0006582387,0.0002092429,0.0008992793,0.0005127329,0.0003153434,0.01761131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002075504,"about_ca_system_score_gemma":0.0001066553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000480425,"about_ca_topic_score_gemma":0.001025237,"domain_scores_codex":[0.9996581,0.00007006712,0.00001266808,0.00007021536,0.0001481001,0.00004087872],"domain_scores_gemma":[0.9995259,0.0001047114,0.00007753478,0.0000834705,0.0001626994,0.00004568008],"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":[0.001221522,0.000178407,0.02503268,0.0003013119,0.00006761093,0.000292524,0.0002023902,0.0009300977,0.07396738,0.002668504,0.04861007,0.8465275],"study_design_scores_gemma":[0.0001501502,0.002291143,0.3910154,0.0005464763,0.0003171172,0.017088,0.002474536,0.1389399,0.2245094,0.01977673,0.2024447,0.0004463958],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4996708,0.003419578,0.3152176,0.004417886,0.002190914,0.0005988376,0.01180424,0.008058272,0.1546218],"genre_scores_gemma":[0.833791,0.001152405,0.09934159,0.001011936,0.0003474599,0.0002258206,0.002414192,0.0002391756,0.06147648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02247245,"threshold_uncertainty_score":0.07517779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03712012867965844,"score_gpt":0.2682624196425863,"score_spread":0.2311422909629279,"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."}}