dc.contributor.author | Shenoy, A. | |
dc.contributor.author | Anthony, S. | |
dc.contributor.author | Frank, R. | |
dc.contributor.author | Davey, N. | |
dc.date.accessioned | 2013-01-14T14:59:18Z | |
dc.date.available | 2013-01-14T14:59:18Z | |
dc.date.issued | 2009 | |
dc.identifier.citation | Shenoy , A , Anthony , S , Frank , R & Davey , N 2009 , Discriminating angry, happy and neutral facial expression : a comparison of computational models . in Communications in Computer and Information Science : Engineering Applications of Neural Networks, Proceedings . vol. 43 , Springer Nature , pp. 200-209 . https://doi.org/10.1007/978-3-642-03969-0_19 | |
dc.identifier.isbn | 978-3-642-03969-0 | |
dc.identifier.isbn | 978-3-642-03968-3 | |
dc.identifier.other | dspace: 2299/5803 | |
dc.identifier.uri | http://hdl.handle.net/2299/9621 | |
dc.description | “The original publication is available at www.springerlink.com” Copyright Springer | |
dc.description.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. | en |
dc.format.extent | 738872 | |
dc.language.iso | eng | |
dc.publisher | Springer Nature | |
dc.relation.ispartof | Communications in Computer and Information Science | |
dc.subject | facial expressions | |
dc.subject | image analysis | |
dc.subject | classification | |
dc.subject | dimensionality reduction | |
dc.title | Discriminating angry, happy and neutral facial expression : a comparison of computational models | en |
dc.contributor.institution | School of Computer Science | |
dc.contributor.institution | Science & Technology Research Institute | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=78049370531&partnerID=8YFLogxK | |
rioxxterms.versionofrecord | 10.1007/978-3-642-03969-0_19 | |
rioxxterms.type | Other | |
herts.preservation.rarelyaccessed | true | |