University of Hertfordshire

Representation and classification of facial expression in a modular computational model

Research output: Chapter in Book/Report/Conference proceedingChapter


  • A. Shenoy
  • T.M. Gale
  • N. Davey
  • R. Frank
View graph of relations
Original languageEnglish
Title of host publicationProceedings of the 11th Neural Computation and Psychology Workshop
EditorsJ. Mayor, N. Ruh, K. Plunkett
PublisherWorld Scientific Publishing
ISBN (Print)9812834222
Publication statusPublished - 2009


Recognizing expressions is a key part of human social interaction; Processing of facial expression information is largely automatic in 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. Here we use two sets of images, namely: Angry and Neutral. Raw face images are examples of high dimensional data, so here we use some dimensionality reduction techniques: Principal Component Analysis and Curvilinear Component Analysis. We 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 also find the effect size of the pixels for the Angry and Neutral faces. We show that it is possible to differentiate faces with a neutral expression from those with an angry 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 6 dimensions.


Copyright World Scientific Publishing Co.

ID: 95213