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    Facial Expression Classification using Kernel Principal Component Analysis and Support Vector Machines


    Ghent, John, Reilly, J. and McDonald, John (2006) Facial Expression Classification using Kernel Principal Component Analysis and Support Vector Machines. In: IMVIP 2006 : Proceedings of the Irish Machine Vision and Image Processing Conference 2006. Vision System Group, DCU, pp. 60-67. ISBN 9780955388507

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    Abstract

    This paper details a novel procedure for accurately classifying lower facial expres- sions. A shape model is developed based on an anatomical analysis of facial expression called the Facial Action Coding System (FACS). This model analyzes the movement in shape due to the formation of a specific expression. We apply Kernel Principal Compo- nent Analysis (KPCA) to the shapes in the training set and classify new unseen expressions by using Support Vector Machines (SVMs). We further analyse our model by attaching a probability measure to the outputs.
    Item Type: Book Section
    Keywords: Facial expression classification, Kernel Principal Component Analysis; KPCA; Support Vector Machines; SVMs;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 8302
    Depositing User: John McDonald
    Date Deposited: 09 Jun 2017 15:33
    Publisher: Vision System Group, DCU
    Refereed: Yes
    Funders: Science Foundation Ireland
    Related URLs:
    URI: https://mural.maynoothuniversity.ie/id/eprint/8302
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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