Recognizing Facial Expressions: A Comparison of Computational Approaches (original) (raw)

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.

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Authors and Affiliations

  1. School of Computer Science, University of Hertfordshire, United Kingdom, AL10 9AB
    Aruna Shenoy, Tim M. Gale, Neil Davey, Bruce Christiansen & Ray Frank
  2. Department of Psychiatry, Queen Elizabeth II Hospital, Welwyn Garden City, Herts, AL7 4HQ, UK
    Tim M. Gale

Authors

  1. Aruna Shenoy
  2. Tim M. Gale
  3. Neil Davey
  4. Bruce Christiansen
  5. Ray Frank

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Véra Kůrková Roman Neruda Jan Koutník

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© 2008 Springer-Verlag Berlin Heidelberg

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Shenoy, A., Gale, T.M., Davey, N., Christiansen, B., Frank, R. (2008). Recognizing Facial Expressions: A Comparison of Computational Approaches. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9\_102

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