@inproceedings{1891e563908f498097f03267f6ff874a,
title = "Gender classification from iris images using fusion of uniform local binary patterns",
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, Gender classification, Iris, LBP",
author = "Tapia, {Juan E.} and Perez, {Claudio A.} and Bowyer, {Kevin W.}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.; 13th European Conference on Computer Vision, ECCV 2014 ; Conference date: 06-09-2014 Through 12-09-2014",
year = "2015",
doi = "10.1007/978-3-319-16181-5_57",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "751--763",
editor = "Carsten Rother and Bronstein, {Michael M.} and Lourdes Agapito",
booktitle = "Computer Vision - ECCV 2014 Workshops, Proceedings",
}