The Human Facial Expression Classification Using the Center Kernel Subspace based the Ridge Regression

The facial expression classification has been implemented on many devices. However, many researchers have conducted the research to improve the classification rate. This research has developed the algorithm to enhance the classification rate on the facial expression field. The proposed method is divided into five primary processes, which are, the first, create the center kernel subspace-based the ridge regression. Secondly, create five scales and eight orientations by using Gabor Filter Bank. The third is to obtain the new signal by using Two-dimensional-Fast Fourier Transform. Fourth, the results are used to build the feature space. It is conducted by the ridge regression of center kernel function. The last process, the primary features can be generated by multiplication between the center kernel and the Eigenvalue. The expression classification can be obtained by using the Mahalanobis method. The proposed method has been evaluated on JAFEE facial expression image database. Experimental shows that the classification rates for the first until the last scenarios are 83.33, 84.03, 86.61, 87.23, 87.24 and 89.79% respectively.