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