6533b836fe1ef96bd12a13eb
RESEARCH PRODUCT
Multilinear sparse decomposition for best spectral bands selection
Sebti FoufouSebti FoufouMongi A. AbidiHamdi Jamel BouchechHamdi Jamel Bouchechsubject
Multilinear mapbusiness.industrysparseMultispectral imagePattern recognitionContext (language use)Spectral bandsSparse approximationMatrix (mathematics)TensorSingular value decompositionMBLBPMultilinearTensorArtificial intelligenceHGPPbusinessSpectral bandsMathematicsdescription
Optimal spectral bands selection is a primordial step in multispectral images based systems for face recognition. In this context, we select the best spectral bands using a multilinear sparse decomposition based approach. Multispectral images of 35 subjects presenting 25 different lengths from 480nm to 720nm and three lighting conditions: fluorescent, Halogen and Sun light are groupped in a 3-mode face tensor T of size 35x25x2 . T is then decomposed using 3-mode SVD where three mode matrices for subjects, spectral bands and illuminations are sparsely determined. The 25x25 spectral bands mode matrix defines a sparse vector for each spectral band. Spectral bands having the sparse vectors with the lowest variation with illumination are selected as the best spectral bands. Experiments on two state-of-the-art algorithms, MBLBP and HGPP, showed the effectiveness of our approach for best spectral bands selection. Scopus
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-01-01 |