• Login
    View Item 
    •   Home
    • IRAC Institute for Research in Applicable Computing - to April 2016
    • Centre for Research in Distributed Technologies (CREDIT)
    • View Item
    •   Home
    • IRAC Institute for Research in Applicable Computing - to April 2016
    • Centre for Research in Distributed Technologies (CREDIT)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UOBREPCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalDepartmentThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalDepartment

    My Account

    LoginRegister

    About

    AboutLearning ResourcesResearch Graduate SchoolResearch InstitutesUniversity Website

    Statistics

    Display statistics

    Categorizing facial expressions: a comparison of computational models

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Authors
    Shenoy, Aruna
    Anthony, Sue
    Frank, Ray
    Davey, Neil
    Issue Date
    2011-09
    
    Metadata
    Show full item record
    Abstract
    Recognizing expressions is a key part of human social interaction, and processing of facial expression information is largely automatic for humans, but it is a non-trivial task for a computational system. The purpose of this work is to develop computational models capable of differentiating between a range of human facial expressions. Raw face images are examples of high-dimensional data, so here we use two dimensionality reduction techniques: principal component analysis and curvilinear component analysis. We also preprocess the images with a bank of Gabor filters, so that important features in the face images may be identified. Subsequently, the faces are classified using a support vector machine. We show that it is possible to differentiate faces with a prototypical expression from the neutral expression. Moreover, we can achieve this with data that has been massively reduced in size: in the best case the original images are reduced to just 5 components. We also investigate the effect size on face images, a concept which has not been reported previously on faces. This enables us to identify those areas of the face that are involved in the production of a facial expression.
    Citation
    Categorizing facial expressions: a comparison of computational models 2011, 20 (6):815-823 Neural Computing and Applications
    Publisher
    Springer Link
    Journal
    Neural Computing and Applications
    URI
    http://hdl.handle.net/10547/250949
    DOI
    10.1007/s00521-010-0446-9
    Additional Links
    http://www.springerlink.com/index/10.1007/s00521-010-0446-9
    Type
    Article
    Language
    en
    ISSN
    0941-0643
    1433-3058
    ae974a485f413a2113503eed53cd6c53
    10.1007/s00521-010-0446-9
    Scopus Count
    Collections
    Centre for Research in Distributed Technologies (CREDIT)

    entitlement

     
    DSpace software (copyright © 2002 - 2021)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.