Recognizing facial expressions: a comparison of computational approaches
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: 1001-1010Publisher
SpringerAdditional Links
http://link.springer.com/chapter/10.1007%2F978-3-540-87536-9_102Type
Book chapterConference papers, meetings and proceedings
Language
enISBN
9783540875352ae974a485f413a2113503eed53cd6c53
10.1007/978-3-540-87536-9_102