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Graph-Preserving Sparse Nonnegative Matrix Factorization With Application to Facial Expression Recognition
KTH, School of Electrical Engineering (EES), Sound and Image Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-7807-5681
KTH, School of Electrical Engineering (EES), Sound and Image Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2011 (English)In: IEEE transactions on systems, man and cybernetics. Part B. Cybernetics, ISSN 1083-4419, E-ISSN 1941-0492, Vol. 41, no 1, 38-52 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, a novel graph-preserving sparse nonnegative matrix factorization (GSNMF) algorithm is proposed for facial expression recognition. The GSNMF algorithm is derived from the original NMF algorithm by exploiting both sparse and graph-preserving properties. The latter may contain the class information of the samples. Therefore, GSNMF can be conducted as an unsupervised or a supervised dimension reduction method. A sparse representation of the facial images is obtained by minimizing the l(1)-norm of the basis images. Furthermore, according to the graph embedding theory, the neighborhood of the samples is preserved by retaining the graph structure in the mapped space. The GSNMF decomposition transforms the high-dimensional facial expression images into a locality-preserving subspace with sparse representation. To guarantee convergence, we use the projected gradient method to calculate the nonnegative solution of GSNMF. Experiments are conducted on the JAFFE database and the Cohn-Kanade database with unoccluded and partially occluded facial images. The results show that the GSNMF algorithm provides better facial representations and achieves higher recognition rates than nonnegative matrix factorization. Moreover, GSNMF is also more robust to partial occlusions than other tested methods.

Place, publisher, year, edition, pages
2011. Vol. 41, no 1, 38-52 p.
Keyword [en]
Facial expression recognition, locality preservation, nonnegative matrix factorization (NMF), sparseness
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-30258DOI: 10.1109/TSMCB.2010.2044788ISI: 000286388300004Scopus ID: 2-s2.0-79551687415OAI: oai:DiVA.org:kth-30258DiVA: diva2:399470
Funder
ICT - The Next Generation
Note
QC 20110222Available from: 2011-02-22 Created: 2011-02-21 Last updated: 2017-12-11Bibliographically approved

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Flierl, Markus

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  • apa
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