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Facial expression recognition based on graph-preserving sparse non-negative matrix factorization
KTH, School of Electrical Engineering (EES), Sound and Image Processing (Closed 130101).ORCID iD: 0000-0002-7807-5681
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Sound and Image Processing (Closed 130101).
2009 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a novel algorithm for representing facial expressions. The algorithm is based on the non-negative matrix factorization (NMF) algorithm, which decomposes the original facial image matrix into two non-negative matrices, namely the coefficient matrix and the basis image matrix. We call the novel algorithm graph-preserving sparse non-negative matrix factorization (GSNMF). GSNMF utilizes both sparse and graph-preserving constraints to achieve a non-negative factorization. The graph-preserving criterion preserves the structure of the original facial images in the embedded sub-space while considering the class information of the facial images. Therefore, GSNMF has more discriminant power than NMF. GSNMF is applied to facial images for the recognition of six basic facial expressions. Our experiments show that GSNMF achieves on average a recognition rate of 93.5% compared to that of discriminant NMF with 91.6%.

Place, publisher, year, edition, pages
2009. 3257-3260 p.
Keyword [en]
Facial expression recognition, non-negative matrix factorization, graph-preserving constraint, sparse representations
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-36092DOI: 10.1109/ICIP.2009.5413940ISI: 000280464301271Scopus ID: 2-s2.0-77951956531OAI: oai:DiVA.org:kth-36092DiVA: diva2:430391
Conference
IEEE International Conference on Image Processing
Note

QC 20110708

Available from: 2011-07-08 Created: 2011-07-08 Last updated: 2016-05-02Bibliographically approved

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Flierl, Markus

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf