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The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning Read chapter Read first chapter

2017 | OriginalPaper | Chapter

9. Gender Classification from NIR Iris Images Using Deep Learning

Authors : Juan Tapia, Carlos Aravena

Published in: Deep Learning for Biometrics

Publisher: Springer International Publishing

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Abstract

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).

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previous chapter DeepGender2: A Generative Approach Toward Occlusion and Low-Resolution Robust Facial Gender Classification via Progressively Trained Attention Shift Convolutional Neural Networks (PTAS-CNN) and Deep Convolutional Generative Adversarial Networks (DCGAN)
next chapter Deep Learning for Tattoo Recognition
Footnotes
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Metadata
Title
Gender Classification from NIR Iris Images Using Deep Learning
Authors
Juan Tapia
Carlos Aravena
Copyright Year
2017
Book
Deep Learning for Biometrics

Print ISBN: 978-3-319-61656-8

Electronic ISBN: 978-3-319-61657-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-61657-5_9

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