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dc.contributor.authorOrun, A.en
dc.contributor.authorSeker, Huseyinen
dc.contributor.authorUslan, Volkanen
dc.contributor.authorGoodyer, E. N.en
dc.contributor.authorSmith, Geoffen
dc.date.accessioned2016-11-08T10:42:10Z
dc.date.available2016-11-08T10:42:10Z
dc.date.issued2016-11-03
dc.identifier.citationOrun, A. et al. (2017) Texture based characterization of sub-skin features by specified laser speckle effects at λ=650nm region. International Journal of Cosmetic Science, 39 (3), pp. 320-326en
dc.identifier.urihttp://hdl.handle.net/2086/12787
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractObjective: The textural structure of “skin age” related sub-skin components enables us to identify and analyse their unique characteristics, thus making substantial progress towards establishing an accurate skin age model. Methods: This is achieved by a two stage process. First by the application of textural analysis using laser speckle imaging, which is sensitive to textural effects within the λ=650 nm spectral band region. In the second stage a Bayesian inference method is used to select attributes from which a predictive model is built. Results: This technique enables us to contrast different skin age models, such as the laser-speckle effect against the more widely used normal light (LED) imaging method, whereby it is shown that our laser speckle based technique yields better results. Conclusion: The method introduced here is non-invasive, low-cost and capable of operating in real-time; having the potential to compete against high-cost instrumentation such as confocal microscopy or similar imaging devices used for skin age identification purposes.en
dc.language.isoenen
dc.publisherJohn Wiley & Sons Inc.en
dc.subjectSkin ageen
dc.subjectlaser speckleen
dc.subjectBayesian networksen
dc.titleTexture based characterization of sub-skin features by specified laser speckle effects at λ=650nm regionen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1111/ics.12379
dc.researchgroupCentre for Computational Intelligence
dc.peerreviewedYesen
dc.explorer.multimediaNoen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NDen
dc.date.acceptance2016-10-31en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.researchinstituteLeicester Institute for Pharmaceutical Innovation - From Molecules to Practice (LIPI)en


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