Recognizing facial expressions: a comparison of computational approaches
| dc.contributor.author | Shenoy, Aruna | en_GB |
| dc.contributor.author | Gale, Tim M. | en_GB |
| dc.contributor.author | Davey, Neil | en_GB |
| dc.contributor.author | Christiansen, Bruce | en_GB |
| dc.contributor.author | Frank, Ray | en_GB |
| dc.date.accessioned | 2013-04-07T19:43:11Z | |
| dc.date.available | 2013-04-07T19:43:11Z | |
| dc.date.issued | 2008 | |
| dc.identifier.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 | en_GB |
| dc.identifier.isbn | 9783540875352 | |
| dc.identifier.doi | 10.1007/978-3-540-87536-9_102 | |
| dc.identifier.uri | http://hdl.handle.net/10547/279195 | |
| dc.description.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. | |
| dc.language.iso | en | en |
| dc.publisher | Springer | en_GB |
| dc.relation.url | http://link.springer.com/chapter/10.1007%2F978-3-540-87536-9_102 | en_GB |
| dc.title | Recognizing facial expressions: a comparison of computational approaches | en |
| dc.type | Book chapter | en |
| dc.type | Conference papers, meetings and proceedings | en |
| html.description.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. |