Image Feature Extraction Using Non Linear Principle Component Analysis

In feature extraction technique for face recognition, to maximize the ratio of between-class scatter to that of within-class scatter and keeps high generalization performance, a nonlinear Evolutionary Weighted Principal Component Analysis (EWPCA) based on Genetic Algorithms is proposed in this paper. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two commonly used feature extraction techniques. However, PCA has many drawbacks. One is that its linearity can limit its relevance to the highly nonlinear systems frequently encountered in face recognition applications. To overcome this problem, nonlinear PCA method has been proposed. Genetic Algorithms (GA) are chosen as the searching method to select optimal weights for the WPCA. In face recognition, Evolutionary facial feature obtained by performing WPCA is used as the representation of original face images. Simulation is done using MATLAB software tool. (c) 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Noorul Islam Centre for Higher Education