On effectiveness of image analysis and recognition by principal component method and linear discriminant analysis

Authors

  • V. V. Mokeyev South Ural State University
  • S. V. Tomilov South Ural State University

Keywords:

face recognition, principal component analysis, linear discriminant analysis, eigenvector

Abstract

In paper, some aspects of image analysis based on principal component analysis and linear discriminant fnalysis are considered. The image recognition technique on base this methods consists of two steps: first we project the face image from the original vector space to a reduced subspace of principal components, second we use LDA to obtain a linear classifier. Main attention is focused on the development of efficient algorithm for computing principal components for large image set. A linear condensation method is used as a new technique to calculate the principal components of a large matrix. To improve the efficiency of the linear condensation method is proposed to use a process of block diagonalization of the matrix. The accuracy and high performance of the developed algorithm is evaluated.

Author Biographies

V. V. Mokeyev, South Ural State University

д-р техн. наук, заведующий кафедрой информационных систем

S. V. Tomilov, South Ural State University

аспирант кафедры информационных систем

References

Kirby, M. Application of the KL procedure for the characterization of human faces / M. Kirby, L. Sirovich // IEEE Trans. Pattern Anal. Mach. Intell. – 1990. – Vol. 12, no. 1. – P. 103–108.

Lu, J. Face Recognition Using LDA-Based Algorithms / J. Lu, K.N. Plataniotis, A.N. Venelsanopoulos // IEEE Trans, on Neural Networks. – 2003. –Vol. 14, no. 1. – P. 195–200.

Martinez, А.М. РСА versus LDA / А.М. Martinez, А.С. Kak // IEEE Trans, on Pattern Analysis and Machine Intelligence. – 2001. – Vol. 23, no. 2. – P. 228–233.

Etemad, K. Discriminant Analysis for Recognition of Human Face Images / K. Etemad, R. Chellappa // Journal of the Optical Society of America A. – 1997. – Vol. 14, no. 8. –P. 1724–1733.

Belhumeur, P.N. Eigenfaces vs.Fisherfaces: recognition using class specific linear projection / P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman // IEEE Trans. Pattern Anal. Mach. Intell. – 1997. – Vol. 19. – P. 711–720.

Гриненко, Н.И. О задачах исследований колебаний конструкций методом конечных элементов / Н.И. Гриненко, В.В. Мокеев // Прикладная механика. – 1985. – 21 (3) – С. 25–30.

Мокеев, В.В. О задаче нахождения собственных значений и векторов больших матричных систем / В.В. Мокеев // Журнал Вычислительной Математики и Математической Физики. – 1992. – 32 (10). – C. 1652–1657.

Мокеев, В.В. О повышение эффективности вычислений главных компонент в задачах анализа изображений / В.В. Мокеев // Цифровая обработка сигналов. –2011. – № 4. – C. 29–36.

The FERET evaluation methodology for face recognition algorithms / P.J. Phillips, H. Moon, P.J. Rauss, S. Rizvi // IEEE Trans. Pattern Anal. Mach. Intell. – 2000.– Vol. 22, no. 10. – P. 1090–1104.

Published

2014-01-29

Issue

Section

The main