Research Based on Improving PCA Face Recognition

Abstract

The Principal Component Analysis (PCA) algorithm is widely used in the field of face recognition because of its high recognition rate and simplicity. The PCA algorithm is based on the principle of Karhunen-Loeve Transformation, because the PCA algorithm is sensitive to outliers, it is improved on the basis of PCA algorithm, combined with Linear Discriminant Analysis (LDA) algorithm, the PCA-LDA face recognition method is proposed. This method obtains the feature space of training sample set by PCA algorithm, On this basis, the LDA algorithm is executed to obtain the feature space of fusion. The PCA is then fused with the LDA's feature space to obtain the new feature space that combines the two. Finally, the face projected in the new feature space is trained and recognized. The experimental results show that the face recognition algorithm proposed in this paper has a higher recognition rate than the traditional PCA algorithm.