Facial expression recognition on partial facial sections

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    6 Citations (Scopus)
    10 Downloads (Pure)

    Abstract

    Research by psychologists have shown that subjects had a preference for a side of a face when it was expressing emotions. This paper seeks to find what accuracies can be attained when only a segment of the face is considered. We show that using one side of the face only reduces accuracy by 0.34% but at half the computationally time required. Various other sections of the face are evaluated for similar performance. We demonstrate that using smaller portions of the face have an expected computation reduction but dont suffer the same degree of accuracy loss. For evaluating we train with a Convolutional Neural Network. To test what portions of a facial image are useful, the full face, half face, eyes, single eye, mouth and half of the mouth are chosen. These images come from the JAFFE, CK+ and KDEF datasets.

    Original languageEnglish
    Title of host publicationISPA 2019 - 11th International Symposium on Image and Signal Processing and Analysis
    EditorsSven Loncaric, Robert Bregovic, Marco Carli, Marko Subasic
    PublisherIEEE Computer Society
    Pages193-197
    Number of pages5
    ISBN (Electronic)9781728131405
    DOIs
    Publication statusPublished - 17 Oct 2019
    Event11th International Symposium on Image and Signal Processing and Analysis, ISPA 2019 - Dubrovnik, Croatia
    Duration: 23 Sep 201925 Sep 2019

    Publication series

    NameInternational Symposium on Image and Signal Processing and Analysis, ISPA
    Volume2019-September
    ISSN (Print)1845-5921
    ISSN (Electronic)1849-2266

    Conference

    Conference11th International Symposium on Image and Signal Processing and Analysis, ISPA 2019
    CountryCroatia
    CityDubrovnik
    Period23/09/1925/09/19

    Keywords

    • CNN
    • Facial Expression Recognition
    • Hemisphere differences
    • Image Processing
    • Neural Network
    • Occlusion

    Fingerprint

    Dive into the research topics of 'Facial expression recognition on partial facial sections'. Together they form a unique fingerprint.

    Cite this