Gender Recognition Using Cognitive Modeling

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    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
    Publication statusPublished - 2012
    Event12th European Conference on Computer Vision (ECCV 2012) - Florence, Italy
    Duration: 7 Oct 201213 Oct 2012
    http://eccv2012.unifi.it/

    Conference

    Conference12th European Conference on Computer Vision (ECCV 2012)
    CountryItaly
    CityFlorence
    Period07/10/201213/10/2012
    Internet address
    SeriesLecture Notes in Computer Science
    Volume7584
    ISSN0302-9743

    Keywords

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

    Cite this

    Fagertun, J., Andersen, T., & Paulsen, R. R. (2012). Gender Recognition Using Cognitive Modeling. In Computer Vision – ECCV 2012: Workshops and Demonstrations, Part II (pp. 300-308). Springer. Lecture Notes in Computer Science, Vol.. 7584 https://doi.org/10.1007/978-3-642-33868-7_30