Discriminating angry, happy and neutral facial expression: a comparison of computational models

A. Shenoy, S. Anthony, R. Frank, N. Davey

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)


    Recognizing expressions are 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 are identified. Subsequently the faces are classified using a Support Vector Machine. We show that it is possible to differentiate faces with a neutral expression from those with a happy expression and neutral expression from those of angry expressions and neutral expression with better accuracy. 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 with happy faces and 5 components with angry faces.
    Original languageEnglish
    Title of host publicationCommunications in Computer and Information Science
    Subtitle of host publicationEngineering Applications of Neural Networks, Proceedings
    PublisherSpringer Nature
    ISBN (Print)978-3-642-03969-0, 978-3-642-03968-3
    Publication statusPublished - 2009


    • facial expressions
    • image analysis
    • classification
    • dimensionality reduction


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