Gender Recognition Using Cognitive Modeling

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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In this work, we use cognitive modeling to estimate the ”gender strength” of frontal faces, a continuous class variable, superseding the traditional binary class labeling. To incorporate this continuous variable we suggest a novel linear gender classification algorithm, the Gender Strength Regression. In addition, we use the gender strength to construct a smaller but refined training set, by identifying and removing ill-defined training examples. We use this refined training set to improve the performance of known classification algorithms. Also the human performance of known data sets is reported, and surprisingly it seems to be quite a hard task for humans. Finally our results are reproduced on a data set of above 40,000 public Danish LinkedIN profile pictures.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2012 : Workshops and Demonstrations, Part II
PublisherSpringer
Publication date2012
Pages300-308
ISBN (print)978-3-642-33867-0
ISBN (electronic)978-3-642-33868-7
DOIs
StatePublished

Conference

Conference12th European Conference on Computer Vision (ECCV 2012)
CountryItaly
CityFlorence
Period07/10/1213/10/12
Internet addresshttp://eccv2012.unifi.it/
NameLecture Notes in Computer Science
Volume7584
ISSN (Print)0302-9743
CitationsWeb of Science® Times Cited: No match on DOI

Keywords

  • Gender recognition, Linear Discriminant Analysis, Support Vector Machines, Cognitive Modeling, Linear Regression
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