A Novel Gabor Filter Selection Based on Spectral Difference and Minimum Error Rate for Facial Expression Recognition

A new feature selection approach is proposed for facial expression recognition system. The features are extracted using Gabor filters from Grey-scale images for characterizing facial texture. Then, an adaptive filter selection (AFS) algorithm is applied to choose the best subset of Gabor filters with different scales and orientations. In AFS algorithm, the filters are selected based on spectral difference between the original image and the noisy image in Gabor wavelet domain. After that, the optimum subset of filters is selected based on minimum error rate. This subset of Gabor filters is used for feature extraction. The extracted features are classified by adopting a multiple linear discriminant analysis (LDA) classifier. Experiments on different databases are carried out that the method is efficient for facial expression recognition.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  Edward R. Dougherty,et al.  An introduction to morphological image processing , 1992 .

[3]  P. Ekman.  Facial expressions of emotion: an old controversy and new findings. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[4]  Michael J. Lyons,et al.  Coding facial expressions with Gabor wavelets , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[5]  Takeo Kanade,et al.  Comprehensive database for facial expression analysis , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[6]  Ioannis Pitas,et al.  Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines , 2007, IEEE Transactions on Image Processing.

[7]  Re Gonzalez,et al.  R.C. Eddins, Digital image processing using MATLAB, vol. Gatesmark Publishing Knoxville , 2009 .

[8]  S. Lajevardi,et al.  Novel higher-order local autocorrelation-like feature extraction methodology for facial expression recognition , 2010 .