Please use this identifier to cite or link to this item:
|Title:||Rotation invariant Facial Expression Recognition in image sequences|
|Keywords:||Facial Expression Recognition|
Gaussian mixture models
|Source:||Srivastava, R., Roy, S., Sim, T. (2010). Rotation invariant Facial Expression Recognition in image sequences. 2010 IEEE International Conference on Multimedia and Expo, ICME 2010 : 179-184. ScholarBank@NUS Repository. https://doi.org/10.1109/ICME.2010.5583363|
|Abstract:||Facial Expression Recognition has mostly been done on frontal or near frontal faces. However, most of the faces in real life are non-frontal. This paper deals with in-plane rotation of faces in image sequences and considers the six universal facial expressions. The proposed approach does not need to rotate the image to frontal position. FER by rotating images to frontal is sensitive to determination of rotation angle and can involve errors in tracking facial points. Directions of motion of Facial Feature Points(FFPs) is used for feature extraction. In training for six expressions, Gaussian Mixture Models are fit to the distribution of angles representing these directions of motion. These models are used for further classification of test sequences using SVM. Gaussian Mixture Modeling is experimentally found to be robust to errors in position of FFPs. For dimensionality reduction, feature selection is performed using Fisher ratio test. © 2010 IEEE.|
|Source Title:||2010 IEEE International Conference on Multimedia and Expo, ICME 2010|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 11, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.