Comparison of Methods of Feature Generation for Face Recognition

Abstract

The paper is concerned with the recognition of faces at application of different methods of global feature generation. We check the selected choice of transformations of images, leading to the numerical representation of the face image. The investigated approaches include the linear and nonlinear methods of transformation: principal component analysis (PCA), Kernel PCA, Fisher linear discriminant analysis (FLD), Sammon transformation and stochastic neighbor embedding with t-distribution (tSNE). The representation of the image in the form of limited number of main components of transformation is put to the input of support vector machine classifier (SVM). The numerical results of experiments will be presented and discussed.