@ARTICLE{26583204_44326814_2011, 
	author = {N. Shchegoleva and Georgy Kukharev}, 
	keywords = {, a two-dimensional Karhunen-Loeve transformation, the use in the business applications, Two-dimensional principal component analysisthe face recognition},
	title = {Algorithms 2DPCA for face recognition},
	journal = {},
	year = {2011},
	number = {4 (18)},
	pages = {31-38},
	url = {https://bijournal.hse.ru/en/2011--4 (18)/44326814.html},
	publisher = {},
	abstract = {In article presents algorithms for two-dimensional principal component analysis (Two-dimensional Principal Component Analysis - 2D PCA)-oriented processing of digital images of large sizes in a small sample. Algorithms based on direct calculation of two covariance matrices for all source images without converting them into vectors. Evaluated characteristics of the presented algorithms. We discuss possibilities presented by the use of algorithms in other areas.},
	annote = {In article presents algorithms for two-dimensional principal component analysis (Two-dimensional Principal Component Analysis - 2D PCA)-oriented processing of digital images of large sizes in a small sample. Algorithms based on direct calculation of two covariance matrices for all source images without converting them into vectors. Evaluated characteristics of the presented algorithms. We discuss possibilities presented by the use of algorithms in other areas.}
}