Abstract
This paper is devoted to the problem of pattern recognition solved by methods of principal components and linear discriminant analysis. The efficiency of the described method is studied for a case when pictures of faces have not yet undergone preliminary processing that would have led them to the standard form (scale, centering, background clipping, brightness adjustment). When processing large sets of images in order to reduce the complexity of computation of principal components, it is proposed to use the linear condensation method and principal component synthesis. We have studied the effectiveness of the approach based on principal component analysis and linear discriminant analysis using the linear condensation method and principal component synthesis on the ORL and FERET database of face images.
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This paper uses the materials of the report submitted at the 11th International Conference “Pattern Recognition and Image Analysis: New Information Technologies,” Samara, Russia, September 23–28, 2013.
Vladimir Viktorovich Mokeev. Born in 1952. In 1988, he defended his candidate’s dissertation at Chelyabinsk Polytechnic Institute, and defended his doctoral dissertation in 1999 at South Ural State University. He achieved the rank of the senior research worker in 1990. At present, he chairs the department of information systems of the South Ural State University. He authored over 100 research works in development and application of numerical methods in various areas of expertise. His latest research work is associated with image analysis and recognition and the analysis and forecasting of socioeconomic systems.
Andrei Vladimirovich Mokeev. Born in 1985. In 2005, he graduated from South Ural State University. In 2005–2008, he was a post-graduate student at the department of information systems at South Ural State University. He has published a number of research works in analysis and recognition of images.
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Mokeev, A.V., Mokeev, V.V. Pattern recognition by means of linear discriminant analysis and the principal components analysis. Pattern Recognit. Image Anal. 25, 685–691 (2015). https://doi.org/10.1134/S1054661815040185
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DOI: https://doi.org/10.1134/S1054661815040185