Global and Feature Based Gender Classification of Faces: A Comparison of Human Performance and Computational Models

S. Buchala, N. Davey, T.M. Gale, R. Frank

    Research output: Contribution to journalArticlepeer-review

    23 Downloads (Pure)

    Abstract

    Most computational models for gender classification use global information (the full face image) giving equal weight to the whole face area irrespective of the importance of the internal features. Here, we use a global and feature based representation of face images that includes both global and featural information. We use dimensionality reduction techniques and a support vector machine classifier and show that this method performs better than either global or feature based representations alone.
    Original languageEnglish
    Pages (from-to)349-360
    JournalProceedings of the Ninth Neural Computation and Psychology Workshop - NCPW
    Volume2004
    Publication statusPublished - 2005

    Fingerprint

    Dive into the research topics of 'Global and Feature Based Gender Classification of Faces: A Comparison of Human Performance and Computational Models'. Together they form a unique fingerprint.

    Cite this