Face recognition using FLDA with single training image per person

DOI10.1016/J.AMC.2008.05.019zbMath1152.68679OpenAlexW2038165640WikidataQ57810763 ScholiaQ57810763MaRDI QIDQ2518469

David Zhang, Lei Zhang, Quan-Xue Gao

Publication date: 16 January 2009

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2008.05.019

zbMATH Keywords

face recognitionsingular value decomposition (SVD)Fisher linear discriminant analysis (FLDA)single training image per person

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for image processing (68U10)

Related Items (8)

A single gallery-based face recognition using extended joint sparse representation ⋮ Fast face recognition based on fractal theory ⋮ A practical application of kernel-based fuzzy discriminant analysis ⋮ Reconstructed error and linear representation coefficients restricted by \(\ell_1\)-minimization for face recognition under different illumination and occlusion ⋮ A new solution to one sample problem in face recognition using FLDA ⋮ Feature extraction based on Laplacian bidirectional maximum margin criterion ⋮ A multi-manifold discriminant analysis method for image feature extraction ⋮ Two-dimensional multiple maximum scatter difference method for face recognition

Uses Software

• Yale Face
• FERET

Cites Work

• Unnamed Item
• A new face recognition method based on SVD perturbation for single example image per person
• Face recognition from a single image per person: a survey
• Face recognition with one training image per person
• A direct LDA algorithm for high-dimensional data -- with application to face recognition

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