Gender classification from NIR iris images using deep learning

Juan Tapia, Carlos Aravena

Resultado de la investigación: Contribución a los tipos de informe/libroCapítulo

16 Citas (Scopus)

Resumen

Gender classification from NIR iris image is a new topic with only a few papers published. All previous work on gender-from-iris tried to find the best feature extraction techniques to represent the information of the iris texture for gender classification using normalized, encoded or periocular images. However this is a new topic in deep-learning application with soft biometric. In this chapter, we show that learning gender-iris representations through the use of deep neural networks may increase the performance obtained on these tasks. To this end, we propose the application of deep-learning methods to separate the gender-from-iris images even when the amount of learning data is limited, using an unsupervised stage with Restricted Boltzmann Machine (RBM) and a supervised stage using a Convolutional Neural Network (CNN).

Idioma originalInglés
Título de la publicación alojadaAdvances in Computer Vision and Pattern Recognition
EditorialSpringer London
Páginas219-239
Número de páginas21
VolumenPartF1
DOI
EstadoPublicada - 2017

Serie de la publicación

NombreAdvances in Computer Vision and Pattern Recognition
VolumenPartF1
ISSN (versión impresa)2191-6586
ISSN (versión digital)2191-6594

Áreas temáticas de ASJC Scopus

  • Software
  • Procesamiento de senales
  • Visión artificial y reconocimiento de patrones
  • Inteligencia artificial

Huella Profundice en los temas de investigación de 'Gender classification from NIR iris images using deep learning'. En conjunto forman una huella única.

  • Citar esto

    Tapia, J., & Aravena, C. (2017). Gender classification from NIR iris images using deep learning. En Advances in Computer Vision and Pattern Recognition (Vol. PartF1, pp. 219-239). (Advances in Computer Vision and Pattern Recognition; Vol. PartF1). Springer London. https://doi.org/10.1007/978-3-319-61657-5_9