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    Recognizing facial expressions: a comparison of computational approaches

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    Authors
    Shenoy, Aruna
    Gale, Tim M.
    Davey, Neil
    Christiansen, Bruce
    Frank, Ray
    Issue Date
    2008
    
    Metadata
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    Abstract
    Recognizing facial expressions are a key part of human social interaction,and processing of facial expression information is largely automatic, 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 some dimensionality reduction techniques: Linear Discriminant Analysis, 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 smiling expression with high 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 11 dimensions.
    Citation
    Shenoy A., Gale T.M., Davey, N., Christansen, B., and Frank, R.J. (2008) 'Recognizing facial expressions: A comparison of Computational approaches', in Artificial Neural Networks - ICANN 2008, series Lecture Notes in Computer Science- Artificial Neural Networks, 5163: 1001-1010
    Publisher
    Springer
    URI
    http://hdl.handle.net/10547/279195
    DOI
    10.1007/978-3-540-87536-9_102
    Additional Links
    http://link.springer.com/chapter/10.1007%2F978-3-540-87536-9_102
    Type
    Book chapter
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9783540875352
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-540-87536-9_102
    Scopus Count
    Collections
    Centre for Research in Distributed Technologies (CREDIT)

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