2.50
Hdl Handle:
http://hdl.handle.net/10547/279195
Title:
Recognizing facial expressions: a comparison of computational approaches
Authors:
Shenoy, Aruna; Gale, Tim M.; Davey, Neil; Christiansen, Bruce; Frank, Ray
Abstract:
Recognizing facial expressions are a key part of human social interaction,and processing of facial expression information is largely automatic, 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 some dimensionality reduction techniques: Linear Discriminant Analysis, 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 smiling 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 11 dimensions.
Citation:
Shenoy A., Gale T.M., Davey, N., Christansen, B., and Frank, R.J. (2008) 'Recognizing facial expressions: A comparison of Computational approaches', in Artificial Neural Networks - ICANN 2008, series Lecture Notes in Computer Science- Artificial Neural Networks, 5163: 1001-1010
Publisher:
Springer
Issue Date:
2008
URI:
http://hdl.handle.net/10547/279195
DOI:
10.1007/978-3-540-87536-9_102
Additional Links:
http://link.springer.com/chapter/10.1007%2F978-3-540-87536-9_102
Type:
Book chapter; Conference papers, meetings and proceedings
Language:
en
ISBN:
9783540875352
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorShenoy, Arunaen_GB
dc.contributor.authorGale, Tim M.en_GB
dc.contributor.authorDavey, Neilen_GB
dc.contributor.authorChristiansen, Bruceen_GB
dc.contributor.authorFrank, Rayen_GB
dc.date.accessioned2013-04-07T19:43:11Z-
dc.date.available2013-04-07T19:43:11Z-
dc.date.issued2008-
dc.identifier.citationShenoy A., Gale T.M., Davey, N., Christansen, B., and Frank, R.J. (2008) 'Recognizing facial expressions: A comparison of Computational approaches', in Artificial Neural Networks - ICANN 2008, series Lecture Notes in Computer Science- Artificial Neural Networks, 5163: 1001-1010en_GB
dc.identifier.isbn9783540875352-
dc.identifier.doi10.1007/978-3-540-87536-9_102-
dc.identifier.urihttp://hdl.handle.net/10547/279195-
dc.description.abstractRecognizing facial expressions are a key part of human social interaction,and processing of facial expression information is largely automatic, 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 some dimensionality reduction techniques: Linear Discriminant Analysis, 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 smiling 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 11 dimensions.en_GB
dc.language.isoenen
dc.publisherSpringeren_GB
dc.relation.urlhttp://link.springer.com/chapter/10.1007%2F978-3-540-87536-9_102en_GB
dc.titleRecognizing facial expressions: a comparison of computational approachesen
dc.typeBook chapteren
dc.typeConference papers, meetings and proceedingsen
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