Please use this identifier to cite or link to this item:
|Title:||Supervised LLE in ICA space for facial expression recognition|
Independent component analysis
|Source:||ICNN&B'05 : 2005 International Conference on Neural Networks & Brain (ICNN&B'05) : Beijing, China, Oct. 13-15, 2005, v. 3, p. 1970-1975 How to cite?|
|Abstract:||Locally linear embedding (LLE) is an unsupervised nonlinear manifold learning algorithm. It performs well in visualizing data yet has a very poor recognition rate in facial expression recognition. In this paper, to improve the performance of LLE in facial expression recognition, we first employ the independent component analysis (ICA) technique to preprocess the face images such that they are represented by some independent components and some noise is filtered from them. We then propose a Supervised LLE (SLLE) algorithm to learn the hidden manifold. SLLE constructs the neighborhood graphs for the data according to the Euclidean distances between them and the cluster information of them. Its embedding step is the same as that of LLE. Finally, we use a generalized regression neural network (GRNN) to learn the implicit nonlinear mapping from the ICA space to the embedded manifold. Experiments on the JAFFE database show promising results.|
|Rights:||© 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
|Appears in Collections:||Conference Paper|
Show full item record
Files in This Item:
|expression-recognition_05.pdf||2.56 MB||Adobe PDF||View/Open|
Citations as of Apr 30, 2016
Checked on Jan 15, 2017
Checked on Jan 15, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.