Discriminating angry, happy and neutral facial expression: a comparison of computational models
| dc.contributor.author | Shenoy, Aruna | en_GB |
| dc.contributor.author | Anthony, Sue | en_GB |
| dc.contributor.author | Frank, Ray | en_GB |
| dc.contributor.author | Davey, Neil | en_GB |
| dc.date.accessioned | 2013-04-07T19:40:25Z | |
| dc.date.available | 2013-04-07T19:40:25Z | |
| dc.date.issued | 2009 | |
| dc.identifier.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 | en_GB |
| dc.identifier.isbn | 9783642039690 | |
| dc.identifier.issn | 1865-0929 | |
| dc.identifier.doi | 10.1007/978-3-642-03969-0_19 | |
| dc.identifier.uri | http://hdl.handle.net/10547/279218 | |
| 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. | |
| dc.language.iso | en | en |
| dc.publisher | Springer | en_GB |
| dc.relation.url | http://link.springer.com/chapter/10.1007%2F978-3-642-03969-0_19 | en_GB |
| dc.title | Discriminating angry, happy and neutral facial expression: a comparison of computational models | en |
| dc.type | Book chapter | en |
| dc.type | Conference papers, meetings and proceedings | en |
| dc.identifier.journal | Communications in Computer and Information Science | en_GB |
| html.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. |