University of Hertfordshire

Categorizing facial expressions: a comparison of computational models

Research output: Contribution to journalArticlepeer-review


  • 904324

    Accepted author manuscript, 910 KB, PDF document

  • Aruna Shenoy
  • Susan Anthony
  • Raymond Frank
  • N. Davey
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Original languageEnglish
Pages (from-to)815-823
JournalNeural Computing and Applications
Publication statusPublished - 2011


Recognizing expressions is 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 may be identified. Subsequently, the faces are classified using a support vector machine. We show that it is possible to differentiate faces with a prototypical expression from the neutral expression. 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. We also investigate the effect size on face images, a concept which has not been reported previously on faces. This enables us to identify those areas of the face that are involved in the production of a facial expression.


The original publication is available at Copyright Springer

ID: 346742