Block-wise 2D kernel PCA/LDA for face recognition

DOI10.1016/j.ipl.2010.06.006zbMath1234.68352OpenAlexW2072626792WikidataQ57576624 ScholiaQ57576624MaRDI QIDQ407537

Armin Eftekhari, Hamid Abrishami Moghaddam, Javad Alirezaie, Mohamad Forouzanfar

Publication date: 27 March 2012

Published in: Information Processing Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ipl.2010.06.006

zbMATH Keywords

computational complexityface recognitionprincipal component analysis (PCA)kernel machineslinear discriminant analysis (LDA)

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (6)

Kronecker-decomposable robust probabilistic tensor discriminant analysis ⋮ Block kernel nonnegative matrix factorization for face recognition ⋮ Emotion recognition using eigenvalues and Levenberg-Marquardt algorithm-based classifier ⋮ A theoretical investigation of feature partitioning principal component analysis methods ⋮ Combined supervised information with PCA via discriminative component selection ⋮ Computational and space complexity analysis of SubXPCA

Uses Software

• ORL face
• PRMLT

Cites Work

• Unnamed Item
• From image vector to matrix: A straightforward image projection technique -- IMPCA vs. PCA
• 1D-LDA vs. 2D-LDA: When is vector-based linear discriminant analysis better than matrix-based?
• (2D)\(^{2}\)LDA: an efficient approach for face recognition

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