3D Facial Skin Texture Analysis Using Geometric Descriptors

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Abstract

We compare skin texture classification using various 2D texture descriptors and their extensions to 3D surface orientation data. We perform a multi-resolution analysis on both the 2D and 3D data. Rotation-Invariant Local Binary Patterns, Multiple Orientations Gabor Filters and Center-Symetric Autocorrelation are used to extract 2D texture features from high resolution facial skin albedo patches. For extracting texture feature directly from the corresponding normal map patches, we propose extensions of these texture measures in both the slant/tilt and tangent spaces. We compare the results of classifying facial wrinkles and pores using the 2D-based and 3D-based texture features. We use the 3DRFE dataset which consists of high resolution 3D facial scans along with the corresponding photometric and albedo images. We notice a net improvement on classifying both wrinkle and pore using the 3D orientation based features over the 2D ones.
Original languageEnglish
Title of host publication22nd International Conference on Pattern Recognition
Place of PublicationStockholm
PublisherIEEE Press
Pages1126 - 1131
Number of pages6
ISBN (Print)978-1-4799-5209-0
DOIs
Publication statusPublished - 28 Aug 2014
Event22nd International Conference on Pattern Recognition - Stockholm Waterfront, Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Publication series

NameInternational Conference on Pattern Recognition
PublisherIEEE Computer Society
ISSN (Electronic)1051-4651

Conference

Conference22nd International Conference on Pattern Recognition
Country/TerritorySweden
CityStockholm
Period24 Aug 201428 Aug 2014

Keywords

  • Texture Analysis
  • Local Binary Patterns
  • Gabor Filters
  • Autocorrelation
  • Face analysis

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