Authors:
Sabry Abdalla M.
1
;
Lubos Omelina
1
;
2
;
Jan Cornelis
1
and
Bart Jansen
1
;
2
Affiliations:
1
Department of Electronics and Informatics, Vrije Universiteit Brussel, Pleinlaan 2 1050 Brussels, Belgium
;
2
imec, Kapeldreef 75, B-3001 Leuven, Belgium
Keyword(s):
Iris Segmentation, Deep Learning, CNN, U-Net, Parameter Optimization.
Abstract:
Segmenting images of the human eye is a critical step in several tasks like iris recognition, eye tracking or pupil tracking. There are a lot of well-established hand-crafted methods that have been used in commercial practice. However, with the advances in deep learning, several deep network approaches outperform the handcrafted methods. Many of the approaches adapt the U-Net architecture for the segmentation task. In this paper we propose some simple and effective new modifications of U-Net, e.g. the increase in size of convolutional kernels, which can improve the segmentation results compared to the original U-Net design. Using these modifications, we show that we can reach state-of-the-art performance using less model parameters. We describe our motivation for the changes in the architecture, inspired mostly by the hand-crafted methods and basic image processing principles and finally we show that our optimized model slightly outperforms the original U-Net and the other state-of-t
he-art models.
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