Discriminating angry, happy and neutral facial expression: a comparison of computational models

2.50
Hdl Handle:
http://hdl.handle.net/10547/279218
Title:
Discriminating angry, happy and neutral facial expression: a comparison of computational models
Authors:
Shenoy, Aruna; Anthony, Sue; Frank, Ray; Davey, Neil
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.
Citation:
Shenoy, A., Anthony, S., Frank, R.J. and Davey, N. (2009) 'Discriminating Angry, Happy and Neutral Facial Expression: A Comparison of Computational Models', in Palmer-Brown, D.; Draganova, C.; Pimenidis, E.; Mouratidis, H. (Eds.) Engineering Applications of Neural Networks - Proceedings 11th International Conference, EANN 2009, series Communications in Computer and Information Science, 43: 200-209
Publisher:
Springer
Journal:
Communications in Computer and Information Science
Issue Date:
2009
URI:
http://hdl.handle.net/10547/279218
DOI:
10.1007/978-3-642-03969-0_19
Additional Links:
http://link.springer.com/chapter/10.1007%2F978-3-642-03969-0_19
Type:
Book chapter; Conference papers, meetings and proceedings
Language:
en
ISSN:
1865-0929
ISBN:
9783642039690
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorShenoy, Arunaen_GB
dc.contributor.authorAnthony, Sueen_GB
dc.contributor.authorFrank, Rayen_GB
dc.contributor.authorDavey, Neilen_GB
dc.date.accessioned2013-04-07T19:40:25Z-
dc.date.available2013-04-07T19:40:25Z-
dc.date.issued2009-
dc.identifier.citationShenoy, A., Anthony, S., Frank, R.J. and Davey, N. (2009) 'Discriminating Angry, Happy and Neutral Facial Expression: A Comparison of Computational Models', in Palmer-Brown, D.; Draganova, C.; Pimenidis, E.; Mouratidis, H. (Eds.) Engineering Applications of Neural Networks - Proceedings 11th International Conference, EANN 2009, series Communications in Computer and Information Science, 43: 200-209en_GB
dc.identifier.isbn9783642039690-
dc.identifier.issn1865-0929-
dc.identifier.doi10.1007/978-3-642-03969-0_19-
dc.identifier.urihttp://hdl.handle.net/10547/279218-
dc.description.abstractRecognizing 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_GB
dc.language.isoenen
dc.publisherSpringeren_GB
dc.relation.urlhttp://link.springer.com/chapter/10.1007%2F978-3-642-03969-0_19en_GB
dc.titleDiscriminating angry, happy and neutral facial expression: a comparison of computational modelsen
dc.typeBook chapteren
dc.typeConference papers, meetings and proceedingsen
dc.identifier.journalCommunications in Computer and Information Scienceen_GB
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