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Gender Classification from NIR Iris Images Using Deep Learning

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Book cover Deep Learning for Biometrics

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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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Notes

  1. 1.

    Biosecure project. http://biosecure.it-sudparis.eu/AB/.

  2. 2.

    https://sites.google.com/a/nd.edu/public-cvrl/data-sets.

  3. 3.

    http://deeplearning.net/software/theano/NEWS.html.

  4. 4.

    https://keras.io/backend/.

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Acknowledgements

Thanks to Vince Thomas, Mike Batanian, Steve Lagree, Yingjie, Bansal and Costa Abreu for the work they have previously done in this research topic and Also to Professor Kevin Bowyer at University of Notre Dame for providing the databases. This work has been supported by Universidad Andres Bello, Faculty of Engineering, Department of Engineering Sciences (DCI).

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Authors and Affiliations

  1. Universidad Andres Bello, Av. Antonio Varas 880, Santiago, Chile

    Juan Tapia

  2. Universidad de Chile, Av. Tupper 2007, Santiago, Chile

    Carlos Aravena

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  1. Juan Tapia
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  2. Carlos Aravena
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Correspondence to Juan Tapia .

Editor information

Editors and Affiliations

  1. University of California, Riverside, California, USA

    Bir Bhanu

  2. Hong Kong Polytechnic University, Hong Kong, China

    Ajay Kumar

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Tapia, J., Aravena, C. (2017). Gender Classification from NIR Iris Images Using Deep Learning. In: Bhanu, B., Kumar, A. (eds) Deep Learning for Biometrics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-61657-5_9

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61656-8

  • Online ISBN: 978-3-319-61657-5

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