@inproceedings{10713d3246e2421a86063b9a24482863,
title = "Multilinear sparse decomposition for best spectral bands selection",
abstract = "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.",
keywords = "HGPP, MBLBP, Multilinear, Spectral bands, Tensor, sparse",
author = "Bouchech, {Hamdi Jamel} and Sebti Foufou and Mongi Abidi",
year = "2014",
doi = "10.1007/978-3-319-07998-1_44",
language = "English (US)",
isbn = "9783319079974",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "384--391",
booktitle = "Image and Signal Processing - 6th International Conference, ICISP 2014, Proceedings",
note = "6th International Conference on Image and Signal Processing, ICISP 2014 ; Conference date: 30-06-2014 Through 02-07-2014",
}