The facial expression recognition method of random forest based on improved PCA extracting feature

In this paper, we propose a new framework for facial expression classification. This framework utilizes random forest as the classifier based on the features extracted from improved principal component analysis (PCA). Traditional PCA has two drawbacks: it is difficult to estimate the covariance matrix, and it is computational prohibitive to get the eigenvectors. In order to solve the two problems, we adopt an improved PCA to extract the features and uses the random forest algorithm as the classifier. The experiments are conducted based on the JAFFE facial expressions library, and are compared with the support vector machine (SVM) classifier. Theoretical analysis and experimental results show that the facial expression recognition system of random forest based on improved PCA extracting feature, whether in terms of facial expression recognition rate or running speed, has shown significant improvement.