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A comparison of the performance of humans and computational models in the classification of facial expression

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

Open access

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. In the first part of the experiment, we develop computational models capable of differentiating between two human facial expressions. We perform pre-processing by Gabor filters and dimensionality reduction using the methods: Principal Component Analysis, and Curvilinear Component Analysis. Subsequently the faces are classified using a Support Vector Machines. We also asked human subjects to classify these images and then we compared the performance of the humans and the computational models. The main result is that for the Gabor pre-processed model, the probability that an individual face was classified in the given class by the computational model is inversely proportional to the reaction time for the human subjects.

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

External Publication IDs

  • handle.net: 10547/279219

Host publication title

nan

Access to documents

paper0094.pdf, opens in new tab
Accepted author manuscript, 64.07 KB

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