The facial expression recognition based on KPCA

Kernel Principal Component Analysis (KPCA) extracting principal component with nonlinear method is an improved PCA. The KPCA has been got widely used in feature extraction and face recognition. The KPCA can extract the feature set which is more suitable in categorization than the conventional PCA. This paper tried to apply the KPCA to feature extraction of facial expression recognition. The experimental results demonstrate that the KPCA is not only good at dimensional reduction, but also available to get better performance than conventional PCA. The highest rate is 97.96%.