Iris Segmentation Using Interactive Deep Learning

Automated iris segmentation is an important component of biometric identification. The role of artificial intelligence, particularly machine learning and deep learning, has been considerable in such automated delineation strategies. Although the use of deep learning is a promising approach in recent times, some of its challenges include its high computational requirement as well as availability of large annotated training data. 

We propose an interactive variant of UNet for iris segmentation, including Squeeze Expand modules, to lower training time while improving storage efficiency through a reduction in the number of parameters involved. The interactive component helps in generating the ground truth for datasets having insufficient annotated samples.

Keywords: Active Learning, Biometrics, Deep Learning, Fine Tuning, Iris Segmentation.