{"id":"W2058138187","doi":"10.1109/conielecomp.2012.6189911","title":"Evaluation of machine learning techniques for face detection and recognition","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Artificial intelligence; Facial recognition system; Preprocessor; Face (sociological concept); Three-dimensional face recognition; Face detection; Biometrics; Object-class detection; Computer vision; Field (mathematics); Pattern recognition (psychology); Identification (biology); 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":[0.009961805,0.001387699,0.001145407,0.003695932,0.0005214255,0.0008995715,0.001144963,0.001334137,0.001835768],"category_scores_gemma":[0.02312824,0.0002108686,0.0009355378,0.001827868,0.0003065723,0.001857234,0.0007200777,0.0007563939,0.0008432789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001103528,"about_ca_system_score_gemma":0.0007306649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003283077,"about_ca_topic_score_gemma":0.00205679,"domain_scores_codex":[0.988577,0.004251196,0.0007600322,0.001044757,0.005026329,0.000340659],"domain_scores_gemma":[0.9760253,0.01642972,0.0007280449,0.001237617,0.005315418,0.0002639587],"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":[0.001372377,0.0006740756,0.01293191,0.0007684513,0.0007018583,0.00008929825,0.00008811425,0.0940588,0.006407781,0.002046794,0.004365967,0.8764946],"study_design_scores_gemma":[0.0001010529,0.002083618,0.02168604,0.0001193231,0.0002318808,0.0003175815,0.0001321767,0.9533647,0.01494076,0.001731353,0.005222958,0.000068646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3917552,0.03497851,0.5456964,0.001084246,0.001556372,0.0009841375,0.002038021,0.004765825,0.01714142],"genre_scores_gemma":[0.7910972,0.005326191,0.1944824,0.0001489593,0.0003298103,0.0004717596,0.003148284,0.0002210941,0.004774362],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009961805,"threshold_uncertainty_score":0.05268365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06189716171133764,"score_gpt":0.309734164209718,"score_spread":0.2478370024983804,"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."}}