Gender classification from iris images using fusion of uniform local binary patterns

Juan E. Tapia, Claudio A. Perez, Kevin W. Bowyer

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

17 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaComputer Vision - ECCV 2014 Workshops, Proceedings
EditorialSpringer Verlag
Páginas751-763
Número de páginas13
Volumen8926
ISBN (versión digital)9783319161808
DOI
EstadoPublicada - 2015
Evento13th European Conference on Computer Vision, ECCV 2014 - Zurich, Suiza
Duración: 6 sep 201412 sep 2014

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen8926
ISSN (versión impresa)03029743
ISSN (versión digital)16113349

Otros

Otros13th European Conference on Computer Vision, ECCV 2014
PaísSuiza
CiudadZurich
Período6/09/1412/09/14

Áreas temáticas de ASJC Scopus

  • Ciencia computacional teórica
  • Informática (todo)

Huella Profundice en los temas de investigación de 'Gender classification from iris images using fusion of uniform local binary patterns'. En conjunto forman una huella única.

  • Citar esto

    Tapia, J. E., Perez, C. A., & Bowyer, K. W. (2015). Gender classification from iris images using fusion of uniform local binary patterns. En Computer Vision - ECCV 2014 Workshops, Proceedings (Vol. 8926, pp. 751-763). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8926). Springer Verlag. https://doi.org/10.1007/978-3-319-16181-5_57