Gaussian decomposition for robust face recognition

Fumihiko Sakaue, Takeshi Shakunaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper discusses Gaussian decomposition of facial images for robust recognition. While it cannot sufficiently extract an effective component, it can decompose an image into two effective components, the filtered image and its residual. The Gaussian component represents rough information for a lighting condition and small individuality. The residual represents individuality and the other information including small noise. The two components complement each other and they are evaluated independently in the framework of eigenface method. The image decomposition can also collaborate with parallel partial projections for robust recognition.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages110-119
Number of pages10
Volume3851 LNCS
DOIs
Publication statusPublished - 2006
Event7th Asian Conference on Computer Vision, ACCV 2006 - Hyderabad, India
Duration: Jan 13 2006Jan 16 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3851 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th Asian Conference on Computer Vision, ACCV 2006
CountryIndia
CityHyderabad
Period1/13/061/16/06

Fingerprint

Face recognition
Face Recognition
Individuality
Decomposition
Decompose
Lighting
Eigenface
Image Decomposition
Rough
Complement
Projection
Partial
Facial Recognition

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Sakaue, F., & Shakunaga, T. (2006). Gaussian decomposition for robust face recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3851 LNCS, pp. 110-119). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3851 LNCS). https://doi.org/10.1007/11612032_12

Gaussian decomposition for robust face recognition. / Sakaue, Fumihiko; Shakunaga, Takeshi.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3851 LNCS 2006. p. 110-119 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3851 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sakaue, F & Shakunaga, T 2006, Gaussian decomposition for robust face recognition. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3851 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3851 LNCS, pp. 110-119, 7th Asian Conference on Computer Vision, ACCV 2006, Hyderabad, India, 1/13/06. https://doi.org/10.1007/11612032_12
Sakaue F, Shakunaga T. Gaussian decomposition for robust face recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3851 LNCS. 2006. p. 110-119. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11612032_12
Sakaue, Fumihiko ; Shakunaga, Takeshi. / Gaussian decomposition for robust face recognition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3851 LNCS 2006. pp. 110-119 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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