Illustration of one iteration of the bilateral constraint domain transform. The black dashed arrow indicates the forward propagation, while the red dashed arrow indicates the back propagation. The red dashed box indicates the iris region, in which the edge map and the coarse segmentation are recursively filtered across rows and columns.
Abstract
Iris semantic segmentation in less-constrained scenarios is the basis of iris recognition. We propose an end-to-end trainable model for iris segmentation, namely Seg-Edge bilateral constraint network (SEN). The SEN uses the edge map and the coarse segmentation to constrain and optimize mutually to produce accurate iris segmentation results. The iris edge map generated from low level convolutional layers passes detailed edge information to iris segmentation, and the iris region generated by high level semantic segmentation constrains the edge filtering scope which makes the edge aware focusing on interesting objects. Moreover, we propose pruning filters and corresponding feature maps that are identified as useless by l1-norm, which results in a lightweight iris segmentation network while keeping the performance almost intact or even better. Experimental results suggest that the proposed method outperforms the state-of-the-art iris segmentation methods.