Gender Classification from Iris Images Using Fusion of Uniform Local Binary Patterns

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)

Abstract

This paper is concerned in analyzing iris texture in order to determine “soft biometric”, attributes of a person, rather than identity. In particular, this paper is concerned with predicting the gender of a person based on analysis of features of the iris texture. Previous researchers have explored various approaches for predicting the gender of a person based on iris texture. We explore using different implementations of Local Binary Patterns from the iris image using the masked information. Uniform LBP with concatenated histograms significantly improves accuracy of gender prediction relative to using the whole iris image. Using a subject-disjoint test set, we are able to achieve over 91 % correct gender prediction using the texture of the iris. To our knowledge, this is the highest accuracy yet achieved for predicting gender from iris texture.

Keywords

Biometrics Iris LBP Gender classification 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Advanced Mining Technology CenterUniversidad de ChileSantiagoChile
  2. 2.Department of Computer Science and EngineeringUniversity of Notre DameNotre DameUSA

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