{"id":"W2106494881","doi":"10.1109/cvpr.2003.1211370","title":"Face alignment using statistical models and wavelet features","year":2003,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Maxima and minima; Face (sociological concept); Gabor wavelet; Computer vision; Feature (linguistics); Wavelet; Facial recognition system; Position (finance); Feature extraction; Statistical model; Wavelet transform; Mathematics; Discrete wavelet transform","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":[0.0007509703,0.0005615796,0.000623912,0.001296287,0.0003165277,0.001057076,0.0008014682,0.0007993318,0.001216663],"category_scores_gemma":[0.002990552,0.0006256317,0.0009699816,0.001186994,0.0005037486,0.001731364,0.0008232445,0.0008185913,0.0008983031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004845806,"about_ca_system_score_gemma":0.0006127249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002590595,"about_ca_topic_score_gemma":0.002711645,"domain_scores_codex":[0.9995294,0.0001104487,0.000019661,0.00009504504,0.0002109226,0.00003454577],"domain_scores_gemma":[0.9992591,0.0003124288,0.0001358093,0.0001513738,0.000115585,0.00002576683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001014628,0.00006144572,0.001277513,0.00004675632,0.00008828293,0.00009993811,0.00006677011,0.6612906,0.01506955,0.02552532,0.001379301,0.294993],"study_design_scores_gemma":[0.000003194411,0.00001297348,0.0001804374,0.000002528737,0.000004371137,0.00002517417,0.000006510734,0.992205,0.00154735,0.005505739,0.0005002918,0.000006435312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006743931,0.00009191266,0.9921539,0.00004474475,0.00001369238,0.0000100578,0.00002372189,0.0003606523,0.0005572797],"genre_scores_gemma":[0.4221307,0.0005828438,0.57289,0.00009436857,0.0000877313,0.0001588763,0.0003545176,0.0003056558,0.00339533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002590595,"threshold_uncertainty_score":0.005151033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03618897173301786,"score_gpt":0.2735719602464188,"score_spread":0.237382988513401,"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."}}