Comparison of Methods of Feature Generation for Face Recognition
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Date issued
2013
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of West Bohemia
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.
Description
Subject(s)
rozpoznávání života, transformace dat, generování znaků
Citation
CPEE – AMTEE 2013: Joint conference Computational Problems of Electrical Engineering and Advanced Methods of the Theory of Electrical Engineering: 4th – 6th September 2013 Roztoky u Křivoklátu, Czech Republic, p. VII-2.