{"id":"W2038216307","doi":"10.1109/icdsp.2011.6004888","title":"Face detection in a compressed domain","year":2011,"lang":"en","type":"preprint","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Discrete cosine transform; Computer science; Lapped transform; Transform coding; Artificial intelligence; Image (mathematics); Computer vision; Face (sociological concept); Focus (optics); Algorithm; Merge (version control); Image compression; Image processing; 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":[0.0002471261,0.0004031246,0.000457242,0.001175096,0.0002054536,0.0005882148,0.0005498454,0.0005677809,0.004530216],"category_scores_gemma":[0.001676353,0.0001670086,0.0002646637,0.0008336008,0.000319814,0.0009125808,0.0006280243,0.0004119278,0.001442547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002348439,"about_ca_system_score_gemma":0.0002666497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001243742,"about_ca_topic_score_gemma":0.00106755,"domain_scores_codex":[0.9995025,0.00004893311,0.00001767298,0.00007718599,0.0003100277,0.00004372799],"domain_scores_gemma":[0.9995327,0.0001426073,0.00003312532,0.00009934382,0.0001708778,0.00002133814],"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":[0.0005959371,0.0001500097,0.0009931726,0.000232137,0.00005522234,0.000744915,0.0001410077,0.03199463,0.2846838,0.01152081,0.006465206,0.6624231],"study_design_scores_gemma":[0.00006270679,0.0002444514,0.002741717,0.00005457233,0.00004496444,0.002292463,0.0001282559,0.7613907,0.2091022,0.009530399,0.01436956,0.00003800063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1260727,0.0009439617,0.8594096,0.0006617897,0.0003192168,0.0001166418,0.0004840635,0.001437857,0.01055415],"genre_scores_gemma":[0.4630252,0.0009060109,0.5235727,0.000432275,0.0003597811,0.0001189698,0.001301593,0.0001523935,0.01013109],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004530216,"threshold_uncertainty_score":0.01515508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0312546934832722,"score_gpt":0.283949193130116,"score_spread":0.2526944996468438,"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."}}