Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patcog.2007.09.015
Title: Cloud basis function neural network: A modified RBF network architecture for holistic facial expression recognition
Authors: De Silva, C.R.
Ranganath, S. 
De Silva, L.C.
Keywords: Facial expression recognition
High-dimensional classifiers
Holistic classification
Human-computer interface
Neural networks
Radial basis functions (RBF)
Issue Date: Apr-2008
Citation: De Silva, C.R., Ranganath, S., De Silva, L.C. (2008-04). Cloud basis function neural network: A modified RBF network architecture for holistic facial expression recognition. Pattern Recognition 41 (4) : 1241-1253. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patcog.2007.09.015
Abstract: The paper presents novel modifications to radial basis functions (RBFs) and a neural network based classifier for holistic recognition of the six universal facial expressions from static images. The new basis functions, called cloud basis functions (CBFs) use a different feature weighting, derived to emphasize features relevant to class discrimination. Further, these basis functions are designed to have multiple boundary segments, rather than a single boundary as for RBFs. These new enhancements to the basis functions along with a suitable training algorithm allow the neural network to better learn the specific properties of the problem domain. The proposed classifiers have demonstrated superior performance compared to conventional RBF neural networks as well as several other types of holistic techniques used in conjunction with RBF neural networks. The CBF neural network based classifier yielded an accuracy of 96.1%, compared to 86.6%, the best accuracy obtained from all other conventional RBF neural network based classification schemes tested using the same database. © 2007 Elsevier Ltd. All rights reserved.
Source Title: Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/55307
ISSN: 00313203
DOI: 10.1016/j.patcog.2007.09.015
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