Promoted PCA and its application in human face recognition

• 1、School of Automation Science and Electrical Engineering, Beihang University

Abstract：Principal component analysis (PCA) is one of accepted and important technique for feature extraction widely used in the areas of images recognition such as human face recognition. Promoted PCA, a human face recognition technique based on PCA, is presented, in order that PCA is not only limited to Gaussian distribution. First, the original training set was divided into some sub-sets. Second the well-known PCA method was directly used to these sub-sets obtained corresponding transformed matrixes. Through these matrixes, training set and testing set were mapped in feature space. In addition, in order to make use of the correlation among samples, a promoted method based on distance-matrix is presented. With this method, the information lost during the blocking is saved and then the performance is improved. To test promoted PCA and to evaluate its performance, a series of experiments were performed on a human face image databases: Feret human face databases. The experimental results indicate that the performance of promoted PCA is obviously superior to that of traditional PCA. The recognition rate is improved.

Keywords： principal component analysis feature extraction promoted principal component analysis feature matrix face recognition