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Illumination and Expression Invariant Recognition Using SSIM Based Sparse Representation

Khwaja, Asim; Asthana, Akshay; Goecke, Roland

Description

The sparse representation technique has provided a new way of looking at object recognition. As we demonstrate in this paper, however, the mean-squared error (MSE) measure, which is at the heart of this technique, is not a very robust measure when it comes to comparing facial images, which differ significantly in luminance values, as it only performs pixel-by-pixel comparisons. This requires a significantly large training set with enough variations in it to offset the drawback of the MSE...[Show more]

dc.contributor.authorKhwaja, Asim
dc.contributor.authorAsthana, Akshay
dc.contributor.authorGoecke, Roland
dc.coverage.spatialIstanbul Turkey
dc.date.accessioned2015-12-10T22:56:56Z
dc.date.createdAugust 23-26 2010
dc.identifier.urihttp://hdl.handle.net/1885/60443
dc.description.abstractThe sparse representation technique has provided a new way of looking at object recognition. As we demonstrate in this paper, however, the mean-squared error (MSE) measure, which is at the heart of this technique, is not a very robust measure when it comes to comparing facial images, which differ significantly in luminance values, as it only performs pixel-by-pixel comparisons. This requires a significantly large training set with enough variations in it to offset the drawback of the MSE measure. A large training set, however, is often not available. We propose the replacement of the MSE measure by the structural similarity (SSIM) measure in the sparse representation algorithm, which performs a more robust comparison using only one training sample per subject. In addition, since the off-the-shelf sparsifiers are also written using the MSE measure, we developed our own sparsifier using genetic algorithms that use the SSIM measure. We applied the modified algorithm to the Extended Yale Face B database as well as to the Multi-PIE database with expression and illumination variations. The improved performance demonstrates the effectiveness of the proposed modifications.
dc.publisherIEEE Computer Society
dc.relation.ispartofseriesInternational Conference on Pattern Recognition (ICPR 2010)
dc.sourceProceedings of the International Conference on Pattern Recognition (ICPR 2010)
dc.subjectKeywords: Facial images; Illumination variation; Luminance value; Mean squared error; Modified algorithms; Sparse representation; Structural similarity; Training sample; Training sets; Algorithms; Object recognition; Pixels; Face recognition
dc.titleIllumination and Expression Invariant Recognition Using SSIM Based Sparse Representation
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2010
local.identifier.absfor080104 - Computer Vision
local.identifier.ariespublicationu4334215xPUB540
local.type.statusPublished Version
local.contributor.affiliationKhwaja, Asim, College of Engineering and Computer Science, ANU
local.contributor.affiliationAsthana, Akshay, College of Engineering and Computer Science, ANU
local.contributor.affiliationGoecke, Roland, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage4
local.identifier.doi10.1109/ICPR.2010.979
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
dc.date.updated2016-02-24T11:01:45Z
local.identifier.scopusID2-s2.0-78149492367
CollectionsANU Research Publications

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