TY - JOUR
T1 - Recognizing Facial Expressions
T2 - A Comparison of Computational Approaches
AU - Shenoy, A.
AU - Gale, T.M.
AU - Davey, N.
AU - Christianson, B.
AU - Frank, R.
N1 - Original article can be found at http://springerlink.com
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=58849144391&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-87536-9_102
DO - 10.1007/978-3-540-87536-9_102
M3 - Article
SN - 0302-9743
VL - 5163
SP - 1001
EP - 1010
JO - Lecture Notes in Computer Science (LNCS)
JF - Lecture Notes in Computer Science (LNCS)
ER -