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

  • Aruna Shenoy
    ,
  • Sue Anthony
    ,
  • Ray Frank
    ,
  • Neil Davey
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

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.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 01/01/2009

Publication status

Published - 01/01/2009

Publisher

Springer, Japan, India, Australia, Germany, United States, United Arab Emirates, Austria, Switzerland, Italy, China, United Kingdom, Netherlands, Brazil, France, Singapore

ISBN (Print)

9783642039690

ISBN (Electronic)

9783642039690

External Publication IDs

  • handle.net: 10547/279218
  • Scopus: 78049370531

Host publication title

nan

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