Localized versus Locality-Preserving Subspace Projections for Face Recognition

被引：0
作者：
IulianB Ciocoiu
HaritonN Costin
机构：
[1] “Gh. Asachi” Technical University of Iaşi,Faculty of Electronics and Telecommunications
[2] “Gr. T. Popa” University of Medicine and Pharmacy,Faculty of Medical Bioengineering
[3] Romanian Academy,Institute for Theoretical Computer Science
来源：
EURASIP Journal on Image and Video Processing | / 2007卷
关键词：
Manifold; Image Processing; Pattern Recognition; Computer Vision; Face Recognition;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Three different localized representation methods and a manifold learning approach to face recognition are compared in terms of recognition accuracy. The techniques under investigation are (a) local nonnegative matrix factorization (LNMF); (b) independent component analysis (ICA); (c) NMF with sparse constraints (NMFsc); (d) locality-preserving projections (Laplacian faces). A systematic comparative analysis is conducted in terms of distance metric used, number of selected features, and sources of variability on AR and Olivetti face databases. Results indicate that the relative ranking of the methods is highly task-dependent, and the performances vary significantly upon the distance metric used.
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