Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patcog.2011.05.006
Title: Facial expression recognition using radial encoding of local Gabor features and classifier synthesis
Authors: Gu, W.
Xiang, C. 
Venkatesh, Y.V. 
Huang, D.
Lin, H. 
Keywords: Classifier synthesis
Facial expression recognition
Fisher linear discriminant
Gabor filter
Human visual cortex
Radial grid encoding
Issue Date: Jan-2012
Source: Gu, W., Xiang, C., Venkatesh, Y.V., Huang, D., Lin, H. (2012-01). Facial expression recognition using radial encoding of local Gabor features and classifier synthesis. Pattern Recognition 45 (1) : 80-91. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patcog.2011.05.006
Abstract: Primarily motivated by some characteristics of the human visual cortex (HVC), we propose a new facial expression recognition scheme, involving a statistical synthesis of hierarchical classifiers. In this scheme, the input images of the database are first subjected to local, multi-scale Gabor-filter operations, and then the resulting Gabor decompositions are encoded using radial grids, imitating the topographical map-structure of the HVC. The codes are fed to local classifiers to produce global features, representing facial expressions. Experimental results show that such a hybrid combination of the HVC structure with a hierarchical classifier significantly improves expression recognition accuracy when applied to wide-ranging databases in comparison with the results in the literature. Furthermore, the proposed system is not only robust to corrupted data and missing information, but can also be generalized to cross-database expression recognition. © 2011 Elsevier Ltd. All rights reserved.
Source Title: Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/50922
ISSN: 00313203
DOI: 10.1016/j.patcog.2011.05.006
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