An Expression Space Model for Facial Expression Analysis
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Local Binary Patterns and Its Application to Facial Image Analysis: A Survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Feature extraction is one of the most important modules for Facial Expression Recognition (FER) systems, which deals with getting the distinguishable features each expression and quantizing it as a discrete symbol. In this paper, we have proposed the novel robust feature extraction technique for the FER systems called Stepwise Linear Discriminant Analysis (SWLDA). This technique focuses on the selection of localized features from the facial expression images and discriminate their classes on the basis of regression values i.e. partial F-test. The proposed technique is then compared with conventional techniques such as LDA in combination with ICA. The results shows that SWLDA better than conventional techniques in terms of robustness in suitable feature selection and classification.