dc.contributor.author | Shenoy, A. | |
dc.contributor.author | Gale, T.M. | |
dc.contributor.author | Davey, N. | |
dc.contributor.author | Frank, R. | |
dc.contributor.editor | Mayor, J. | |
dc.contributor.editor | Ruh, N. | |
dc.contributor.editor | Plunkett, K. | |
dc.date.accessioned | 2013-01-14T14:59:16Z | |
dc.date.available | 2013-01-14T14:59:16Z | |
dc.date.issued | 2009 | |
dc.identifier.citation | Shenoy , A , Gale , T M , Davey , N & Frank , R 2009 , Representation and classification of facial expression in a modular computational model . in J Mayor , N Ruh & K Plunkett (eds) , Proceedings of the 11th Neural Computation and Psychology Workshop . World Scientific Publishing , pp. 141-152 . | |
dc.identifier.isbn | 9812834222 | |
dc.identifier.other | PURE: 95213 | |
dc.identifier.other | PURE UUID: 723811c3-09a1-4896-993c-f5a3470c8e32 | |
dc.identifier.other | dspace: 2299/3964 | |
dc.identifier.uri | http://hdl.handle.net/2299/9620 | |
dc.description | Copyright World Scientific Publishing Co. | |
dc.description.abstract | 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. | en |
dc.language.iso | eng | |
dc.publisher | World Scientific Publishing | |
dc.relation.ispartof | Proceedings of the 11th Neural Computation and Psychology Workshop | |
dc.title | Representation and classification of facial expression in a modular computational model | en |
dc.contributor.institution | School of Computer Science | |
dc.contributor.institution | Science & Technology Research Institute | |
rioxxterms.type | Other | |
herts.preservation.rarelyaccessed | true | |