{"id":"W2048359624","doi":"10.1007/s00138-013-0579-9","title":"Fully automatic expression-invariant face correspondence","year":2013,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Face recognition and analysis","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Artificial intelligence; Computer science; Invariant (physics); Face (sociological concept); Point (geometry); Computer vision; Pattern recognition (psychology); Expression (computer science); Set (abstract data type); Facial expression; Mathematics; Geometry","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":[0.0003817072,0.0009090963,0.001194926,0.0009692397,0.0005368234,0.001145946,0.001427589,0.0008797224,0.009101097],"category_scores_gemma":[0.0009758761,0.000582292,0.0008511758,0.0009669008,0.0003951871,0.001123744,0.00203976,0.00118863,0.007882947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003403571,"about_ca_system_score_gemma":0.0009996892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001208489,"about_ca_topic_score_gemma":0.002351007,"domain_scores_codex":[0.9988703,0.0001363852,0.00002966156,0.0003501419,0.0004370552,0.0001764571],"domain_scores_gemma":[0.9996207,0.00003926063,0.00002452129,0.0001810303,0.000113549,0.00002084323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003905016,0.0001956302,0.0007588806,0.00009334592,0.0000664283,0.0001425964,0.00005240223,0.01479414,0.167236,0.005660037,0.01062208,0.799988],"study_design_scores_gemma":[0.00005724174,0.0002007513,0.004240483,0.00002759606,0.00005225687,0.001077584,0.00008913249,0.7525219,0.2122828,0.01437467,0.01502343,0.00005215161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02916454,0.0002392786,0.9563468,0.0001452737,0.0001515775,0.00009377275,0.0005962646,0.006389827,0.006872706],"genre_scores_gemma":[0.426794,0.0004660245,0.5429885,0.0002563002,0.0001427634,0.000208732,0.00313572,0.001722563,0.02428538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009101097,"threshold_uncertainty_score":0.03044617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007117041141617277,"score_gpt":0.2528260352248051,"score_spread":0.2457089940831879,"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."}}