{"id":"W4236097319","doi":"10.32920/ryerson.14656041","title":"Safely caching HOG pyramid feature levels, to speed up facial landmark detection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Pyramid (geometry); Landmark; Overhead (engineering); Feature (linguistics); Histogram of oriented gradients; Histogram; Cache; Set (abstract data type); Artificial intelligence; Encoding (memory); Frame (networking); Bayesian network; Pattern recognition (psychology); Data set; Computer vision; Image (mathematics); Data mining; Computer network; Mathematics","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":[0.0002746264,0.0005560786,0.0004469378,0.0005878813,0.0004466518,0.0008046926,0.00120525,0.0003769133,0.004705359],"category_scores_gemma":[0.001487781,0.0003585976,0.0002652816,0.0006218283,0.0003386007,0.001422284,0.0006903352,0.000565131,0.00192291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007626034,"about_ca_system_score_gemma":0.001615735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01181828,"about_ca_topic_score_gemma":0.02056639,"domain_scores_codex":[0.9997137,0.0000234921,0.00001629291,0.00009040069,0.0001145269,0.00004153309],"domain_scores_gemma":[0.9996481,0.00008477374,0.00004600817,0.0001206199,0.00007470627,0.0000257545],"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":[0.000412301,0.0001776657,0.003036084,0.00006614556,0.0000370355,0.0001748358,0.00009698208,0.039738,0.07361129,0.0036552,0.01126596,0.8677285],"study_design_scores_gemma":[0.00004720287,0.0001494162,0.002252286,0.00001339166,0.00002909655,0.0001990303,0.00005478246,0.9130338,0.07193083,0.004394288,0.00786769,0.00002813617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09480958,0.0004559967,0.8855451,0.0002807032,0.0001556195,0.0001438935,0.0004315054,0.01435972,0.003817814],"genre_scores_gemma":[0.4698677,0.0002742859,0.5197868,0.0001419539,0.00006872501,0.00009338564,0.0009108019,0.0005468348,0.008309511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01181828,"threshold_uncertainty_score":0.02349901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03104042598846666,"score_gpt":0.268314657159052,"score_spread":0.2372742311705854,"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."}}