Median null([formula omitted])-based method for face feature recognition

Abstract

With the progress of science and technology artificial intelligence is being paid more and more attention. People want to use computers to deal with complex practical problems. So, linear discriminant analysis (LDA) is widely used as a dimensionality reduction technique in image and text recognition classification tasks. However, a weakness of LDA model is that the class average vector in the formula completely depends on class sample average. Under special circumstances such as noise, bright light, some outliers will appear in the practical input databases. Therefore, by employing several given practical samples, the class sample average is not enough to estimate the class average accurately. So, the recognition performance of LDA model will decline. Compared to human intelligence, computers are far short of necessary fundamental knowledge of judgment which people normally acquire during the formative years of their lives. In order to solve the problem and also to render LDA model more robust, we propose a within-class scatter matrix null space median method (M-N(Sw)), which first transforms the original space by employing a basis of within-class scatter matrix null space, and then in the transformed space the maximum of between-class scatter matrix is pursued. In the second stage, within-class median vector is used in the traditional LDA model. Experiments on ORL, FERET and Yale face data sets are performed to test and evaluate the effectiveness of the proposed method.